Posts Tagged BCI
We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Speciﬁcally, we show that BCI technology can be used to learn personalized decoding models that relate the global conﬁguration of brain rhythms in individual subjects (as measured by EEG) to their motor performance during 3D reaching movements. We demonstrate that our models capture substantial across-subject heterogeneity, and argue that this heterogeneity is a likely cause of limited effect sizes observed in TES for enhancing motor performance. We conclude by discussing how our personalized models can be used to derive optimal TES parameters, e.g., stimulation site and frequency, for individual patients.
Motor deﬁcits are one of the most common outcomes of stroke. According to the World Health Organization, 15 million people worldwide suffer a stroke each year. Of these, ﬁve million are permanently disabled. For this third, upper limb weakness and loss of hand function are among the most devastating types of disabilities, which affect the quality of their daily life . Despite a wide range of rehabilitation therapies, including medication treatment , conventional physiotherapy , and robot physiotherapy , only approximately 20% of patients achieve some form of functional recovery in the ﬁrst six months , .
Current research on novel therapies includes neurofeedback training based on brain-computer interface (BCI) technology and transcranial electrical stimulation (TES). The former approach attempts to support cortical reorganization by providing haptic feedback with a robotic exoskeleton that is congruent to movement attempts, as decoded in real-time from neuroimaging data , . The latter type of research aims to reorganize cortical networks in a way that supports motor performance, because post-stroke alterations of cortical networks have been found to correlate with the severity of motor deﬁcits , . While initial evidence suggested that both approaches, BCIbased training  and TES , have a positive impact, the signiﬁcance of these results over conventional physiotherapy was not always achieved by different studies , , .
One potential explanation for the difﬁculty to replicate the initially promising ﬁndings is the heterogeneity of stroke patients. Different locations of stroke-induced structural changes
are likely to result in substantial across-patient variance in the functional reorganization of cortical networks. As a result, not all patients may beneﬁt from the same neurofeedback or stimulation protocol. We thus propose to fuse these two research themes and use BCI technology to learn personalized models that relate the conﬁguration of cortical networks to each patient’s motor deﬁcits. These personalized models may then be used to predict which TES parameters, e.g., spatial location and frequency band, optimally support rehabilitation in each individual patient.
In this study, we address the ﬁrst step towards personalized TES for stroke rehabilitation. Using a transfer learning framework developed in our group , we show how to create personalized decoding models that relate the EEG of healthy subjects during a 3D reaching task to their motor performance in individual trials. We further demonstrate that the resulting decoding models capture substantial acrosssubject heterogeneity, thereby providing empirical support for the need to personalize models. We conclude by reviewing our ﬁndings in the light of TES studies to improve motor performance in healthy subjects, and discuss how personalized TES parameters may be derived from our models.[…]
Objective: Apply BCI technology to improve stroke rehabilitation therapy
Background: Brain-computer interfaces (BCI) measure brain activity to generate control signals for external devices in real-time. BCIs are especially well suited for motor rehabilitation. Motor imagery BCIs can analyze patients’ sensorimotor regions and control conditionally gated feedback devices that allow the patient to regain motor functions.
Design/Methods: Patients with sub-acute stroke were trained for 25 30-minute sessions in which they imagined left or right hand movement. A computer avatar indicated which hand the patient should imagine moving (80 trials left hand; 80 trials right). The BCI system analyzed EEG in real time, deciphered intention for left or right hand movement, and triggered functional electrical stimulation that elicited movement in the corresponding hand and in the computer avatar only when the patient produced the correct corresponding EEG pattern. Motor function improvements were assessed with a 9-hole PEG test.
Results: In a chronic stroke patient the 9-hole PEG test showed an improvement in affected left hand movement from 1 min 30 seconds to 52 sec after 24 training sessions (healthy right hand: 26 sec). BCI accuracy increased from 70% to 98.5 % across sessions. Mean accuracy for the first 3 sessions was 81%; 88% for the last 3. Before training, the patient could not lift his affected arm. After training the patient could reach his mouth to feed himself.
Conclusions: BCI accuracy is an objective marker of a patient’s participation in the task; 50% means that patient doesn’t follow (or cannot follow) the task. This patient’s continued improvement and high final accuracy indicates motivated participation. Most importantly, there was objective improvement in motor function within only 25 training sessions. We attribute these results to the conditionally gated reward from the BCI (inducing Hebbian plasticity), and mirror neuron system activation by the avatar.
Disclosure: Dr. Guger has received personal compensation for activities with g.tec Medical Engineering GmbH as an employee. Dr. Coon has nothing to disclose. Dr. Swift has nothing to disclose.
For these people, neuroprosthetic devices can offer some much-needed hope.
Brain-computer interfaces (BCI) usually involve electrodes – placed on the human skull, on the brain’s surface, or in the brain’s tissue – that monitor and measure the brain activity that occurs when the brain “thinks” a thought. The pattern of this brain activity is then “translated” into a code, or algorithm, which is “fed” into a computer. The computer, in turn, transforms the code into commands that produce movement.
Neuroprosthetics are not just useful for people who cannot move their arms and legs; they also help those with sensory disabilities. The World Health Organization (WHO) estimate that approximately 360 million people across the globe have a disabling form of hearing loss, while another 39 million people are blind.
For some of these people, neuroprosthetics such as cochlear implants and bionic eyes have given them back their senses and, in some cases, they have enabled them to hear or see for the very first time.
Here, we review five of the most significant developments in neuroprosthetic technology, looking at how they work, why they are helpful, and how some of them will develop in the future.
Probably the “oldest” neuroprosthetic device out there, cochlear implants (or ear implants) have been around for a few decades and are the epitome of successful neuroprosthetics.
The U.S. Food and Drug Administration (FDA) approved cochlear implants as early as 1980, and by 2012, almost 60,000 U.S. individuals had had the implant. Worldwide, more than 320,000 people have had the device implanted.
A cochlear implant works by bypassing the damaged parts of the ear and stimulating the auditory nerve with signals obtained using electrodes. The signals relayed through the auditory nerve to the brain are perceived as sounds, although hearing through an ear implant is quite different from regular hearing.
Although imperfect, cochlear implants allow users to distinguish speech in person or over the phone, with the media abound with emotional accounts of people who were able to hear themselves for the first time using this sensory neuroprosthetic device.
Here, you can watch a video of a 29-year-old woman who hears herself for the first time using a cochlear implant:
The first artificial retina – called the Argus II – is made entirely from electrodes implanted in the eye and was approved by the FDA in February 2013. In much the same way as the cochlear implant, this neuroprosthetic bypasses the damaged part of the retina and transmits signals, captured by an attached camera, to the brain.
This is done by transforming the images into light and dark pixels that get turned into electrical signals. The electrical signals are then sent to the electrodes, which, in turn, send the signal to the brain’s optic nerve.
While Argus II does not restore vision completely, it does enable patients with retinitis pigmentosa – a condition that damages the eye’s photoreceptors – to distinguish contours and shapes, which, many patients report, makes a significant difference in their lives.
Retinitis pigmentosa is a neurodegenerative disease that affects around 100,000 people in the U.S. Since its approval, more than 200 patients with retinitis pigmentosa have had the Argus II implant, and the company that designed it is currently working to make color detection possible as well as improve the resolution of the device.
Neuroprosthetics for people with SCI
Almost 350,000 people in the U.S. are estimated to live with SCI, and 45 percent of those who had an SCI since 2010 are considered tetraplegic – that is, paralyzed from the neck down.
Bill Kochevar had electrodes surgically fitted into his brain. After training the BCI to “learn” the brain activity that matched the movements he thought about, this activity was turned into electrical pulses that were then transmitted back to the electrodes in his brain.
In much the same way that the cochlear and visual implants bypass the damaged area, so too does this BCI area avoid the “short circuit” between the brain and the patient’s muscles created by SCI.
With the help of this neuroprosthetic, the patient was able to successfully drink and feed himself. “It was amazing,” Kochevar says, “because I thought about moving my arm and it did.” Kochevar was the first patient in the world to test the neuroprosthetic device, which is currently only available for research purposes.
You can learn more about this neuroprosthetic from the video below:
However, this is not where SCI neuroprosthetics stop. The Courtine Lab – which is led by neuroscientist Gregoire Courtine in Lausanne, Switzerland – is tirelessly working to help injured people to regain control of their legs. Their research efforts with rats have enabled paralyzed rodents to walk, achieved by using electrical signals and making them stimulate nerves in the severed spinal cord.
“We believe that this technology could one day significantly improve the quality of life of people confronted with neurological disorders,” says Silvestro Micera, co-author of the experiment and neuroengineer at Courtine Labs.
Recently, Prof. Courtine has also led an international team of researchers to successfully create voluntary leg movement in rhesus monkeys. This was the first time that a neuroprosthetic was used to enable walking in nonhuman primates.
However, “it may take several years before all the components of this intervention can be tested in people,” Prof. Courtine says.
An arm that feels
Silvestro Micera has also led other projects on neuroprosthetics, among which is the arm that “feels.” In 2014, MNT reportedon the first artificial hand that was enhanced with sensors.
Researchers measured the tension in the tendons of the artificial hand that control grasping movements and turned it into electric current. In turn, using an algorithm, this was translated into impulses that were then sent to the nerves in the arm, producing a sense of touch.
Since then, the prosthetic arm that “feels” has been improved even more. Researchers from the University of Pittsburgh and the University of Pittsburgh Medical Center, both in Pennsylvania, tested the BCI on a single patient with quadriplegia: Nathan Copeland.
The scientists implanted a sheath of microelectrodes below the surface of Copeland’s brain – namely, in his primary somatosensory cortex – and connected them to a prosthetic arm that was fitted with sensors. This enabled the patient to feel sensations of touch, which felt, to him, as though they belonged to his own paralyzed hand.
While blindfolded, Copeland was able to identify which finger on his prosthetic arm was being touched. The sensations he perceived varied in intensity and were felt as differing in pressure.
Neuroprosthetics for neurons?
We have seen that brain-controlled prosthetics can restore patients’ sense of touch, hearing, sight, and movement, but could we build prosthetics for the brain itself?
Researchers from the Australian National University (ANU) in Canberra managed to artificially grow brain cells and create functional brain circuits, paving the way for neuroprosthetics for the brain.
By applying nanowire geometry to a semiconductor wafer, Dr. Vini Gautam, of ANU’s Research School of Engineering, and colleagues came up with a scaffolding that allows brain cells to grow and connect synaptically.
Project group leader Dr. Vincent Daria, from the John Curtin School of Medical Research in Australia, explains the success of their research:
“We were able to make predictive connections between the neurons and demonstrated them to be functional with neurons firing synchronously. This work could open up a new research model that builds up a stronger connection between materials nanotechnology with neuroscience.”
Neuroprosthetics for the brain might one day help patients who have experienced a stroke or who live with neurodegenerative diseases to recover neurologically.
Every year in the U.S., almost 800,000 people have had a stroke, and more than 130,000 people die from it. Neurodegenerative diseases are also widespread, with 5 million U.S. adults estimated to live with Alzheimer’s disease, 1 million to have Parkinson’s, and 400,000 to experience multiple sclerosis.
The social media company’s 60-strong team hopes to achieve this miraculous feat using optical imaging that scans the brain hundreds of times per second, detecting our silent internal dialogues and translating them into text on a screen.
They hope that, eventually, the technology will allow users to type at 100 words per minute – five times faster than typing on a phone.
If this innovation comes to pass, it will be fascinating for Facebook’s following. There will, however, be deeper and more profound ramifications for people who do not have full use of their limbs.
Brain-computer interfaces (BCIs) that allow users to type with their minds are already available, but they are either slow or require a sensor to be implanted in the brain. This procedure is expensive, risky, and not likely to be adopted by the population at large.
If so-called brain typing could be perfected without the need for intrusive implants, it would be a genuine game-changer with a whole host of applications.
BCIs, then and now
The first steps toward developing a BCI came with Hans Berger’s discovery that the brain was electrically active. Each time an individual nerve cell sends a message, it is accompanied by a tiny electrical signal that nips from neuron to neuron.
This electrical signal can be picked up outside of the skull using an electroencephalogram (EEG). Berger was the first person to record human brain activity using an EEG, having achieved this feat almost a century ago, in 1924.
The term “brain-computer interface” was coined in the 1970s, in papers written by scientists from the University of California-Los Angeles. The research was led by Jacques Vidal, who is now considered the grandfather of BCI.
“Can these observable electrical brain signals be put to work as carriers of information in man-computer communication or for the purpose of controlling such external apparatus as prosthetic devices or spaceships?”
Jacques Vidal, “Toward direct brain-computer communication,” 1973
Of course, animal studies were the first port of call when investigating BCIs. Research in the late 1960s and early 1970s proved that monkeys could learn to control the firing rates of single neurons or groups of neurons in the primary motor cortex if they were given a reward. Similarly, using operant conditioning, dogs could be trained to control the rhythms in their hippocampus.
These early studies showed that the electrical output of the brain could be measured and manipulated. Over the past two decades, there has been a surge of interest in BCIs. There is still a long way to go, but there have been notable successes.
In modern BCIs, the cream of the experimental crop is a recently designed system from Stanford University. Two aspirin-sized implants, inserted into an individual’s brain, chart the activity of the motor cortex – a region that controls muscles. Algorithms then interpret this activity and convert it into cursor movements on a screen.
In a recent study, one participant was able to type 39 characters (around eight words) per minute. “This study reports the highest speed and accuracy, by a factor of three, over what’s been shown before,” says Krishna Shenoy, one of the senior authors.
Invasive, semi-invasive, and noninvasive
Broadly speaking, modern BCIs are split into three groups. These are:
- Invasive BCIs: Implants are placed directly into the brain. Software is trained to interpret a subject’s brain activity. For instance, a computer cursor can be controlled by a participant’s thoughts of “left,” “right,” “up,” and “down.” With enough practice, a user can draw shapes on a screen, control a television, and open computer programs.
- Semi-invasive BCIs: This type of device is implanted inside the skull but does not sit within the gray matter itself. Although less invasive than an invasive BCI, implants left under the skull for long periods of time tend to form scar tissue in the gray matter, which, eventually, blocks the signals and renders them unusable.
- Noninvasive BCIs: These work on the same principle, but do not involve surgical implantation and have, therefore, received the most research.
Of the noninvasive BCIs, the most common type are EEG-based BCIs. These read the electrical activity of the brain from outside of the body. However, because the skull scatters the electrical signals substantially, making them accurate is a real challenge. Added to this issue, they often take a fair amount of calibration before each use. That being said, there have been some significant steps forward over recent years.
For instance, some researchers have recently investigated noninvasive BCIs as a way to help individuals with amyotrophic lateral sclerosis and brain stem stroke. These patients can become “locked in,” meaning that they lose the use of all voluntary muscles and, as such, have no way to communicate, despite being cognitively “normal.”
Their studies led them to conclude that “BCI use may be of benefit to those with locked-in syndrome.”
How do noninvasive BCIs work?
BCI technology is based on detecting electrical activity emanating from the brain and then converting it into an external action. However, through the cacophony of neural noise, which signals should be paid attention to?
There are a number of signal types that noninvasive BCIs use, the most popular of which is the P300 event-related potential.
An event-related potential is a measurable brain response to a particular stimulus – specifically, the P300 is produced during decision-making and it is usually elicited experimentally using the so-called oddball paradigm.
They are asked to look out for a specific symbol that occurs only rarely within the selection. When the target symbol is noticed by the participant, it triggers a P300 wave.
Over many trials, it is possible to distinguish the P300 from other electrical signals; it is easiest to observe emanating from the parietal lobe, a part of the brain responsible, in part, for integrating sensory information.
Once an algorithm is trained to recognize an individual’s P300, it can, from then on, understand what they are looking for. For instance, if the user is typing a word and they wish to start with the letter “a,” when that letter appears on the screen, a P300 will be generated by the brain, the software will recognize it, and the letter “a” is typed on the screen.
Compared with other similar methods, P300s are relatively fast, require little training (hours rather than days), and are effective for most users.
However, there are still shortfalls. Because the system needs to pick up a user’s response to individual characters, it has to run through a list before it can find the right one. This means that there is a limit to how fast one can type.
There are ways to minimize this wait, but the time taken is still longer than researchers (and users) would like.
How will Facebook achieve 100 words per minute?
To make a system that can type tens of words per minute, a new step in the process will be needed – in fact, an entirely new approach will be necessary, and that is what Facebook is working on.
Medical News Today spoke with Dr. Michael M. Merzenich, chief scientific officer of Posit Science and co-inventor of the cochlear implant. We asked how Facebook’s researchers will bypass this speed issue, to which he responded, “Facebook has discussed using near-infrared (NIR) imaging technology.” With this technology, each word will be picked out in one go, rather than being spelled out letter by letter.
Of course, this comes with its own difficulties. Dr. Merzenich added:
“While it’s very easy to type ‘lion’ versus ‘tiger’ and be clear, it’s going to be quite a bit harder to have a noninvasive brain imaging technology detect minute differences in brain activity that may correspond to small differences in a category like that.”
“Thinking of the word ‘lion’ and the word ‘tiger’ activates extremely similar and overlapping networks of brain activity for most people.”
There is clearly a lot of work yet to do, but Dr. Merzenich is confident that it will be achieved eventually. He added:
“The best hope is to use modern AI [artificial intelligence] techniques – deep learning techniques – that will gradually learn to identify the patterns of brain activity for an individual person as meaning specific things.”
“In this way, I think it’s likely that people will individually train their brain-reading systems, and those systems will be individually attuned to them and not immediately transferable to another person. In fact, people using these systems will likely train their own brains to optimally produce readable signals to these systems. In this way, these systems represent another application of brain plasticity – the ability of the brain to change itself through training.”
This may all be a long way off, but Facebook are committed; they are combining their research power with a number of universities across the United States. The future looks bright for BCIs and, if they do achieve 100 words per minute, it will be a great leap for millions of people who are unable to communicate with ease.
[ARTICLE] Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power – Full Text
Transcranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation. Considering that the cerebellum and motor cortex exert influence on the motor network, their stimulation could enhance motor functions, such as motor imagery, and be utilized for brain-computer interfaces (BCIs) during motor neurorehabilitation.
A new tDCS montage that influences cerebellum and either right-hand or feet motor area is proposed and validated with a simulation of electric field. The effect of current density (0, 0.02, 0.04 or 0.06 mA/cm2) on electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on 5 healthy volunteers for different stimulation modalities: 1) 10-minutes anodal tDCS before EEG acquisition over right-hand or 2) feet motor cortical area, and 3) 4-seconds anodal tDCS during EEG acquisition either on right-hand or feet cortical areas before each time right-hand or feet motor imagery is performed. For each subject and tDCS modality, analysis of variance and Tukey-Kramer multiple comparisons tests (p <0.001) are used to detect significant differences between classification accuracies that are obtained with different current densities. For tDCS modalities that improved accuracy, t-tests (p <0.05) are used to compare μ and βband power when a specific current density is provided against the case of supplying no stimulation.
The proposed montage improved the classification of right-hand motor imagery for 4 out of 5 subjects when the highest current was applied for 10 minutes over the right-hand motor area. Although EEG band power changes could not be related directly to classification improvement, tDCS appears to affect variably different motor areas on μ and/or β band.
The proposed montage seems capable of enhancing right-hand motor imagery detection when the right-hand motor area is stimulated. Future research should be focused on applying higher currents over the feet motor cortex, which is deeper in the brain compared to the hand motor cortex, since it may allow observation of effects due to tDCS. Also, strategies for improving analysis of EEG respect to accuracy changes should be implemented.
Transcranial direct current stimulation (tDCS) is a noninvasive technique for brain stimulation where direct current is supplied through two or more electrodes in order to modulate temporally brain excitability [1, 2]. This technique has shown potential to improve motor performance and motor learning [3, 4, 5]. Hence, it could be applied in motor neurorehabilitacion . However, tDCS effects vary depending on several factors, such as the size or position of the stimulation electrodes and the current intensity that is applied  or the mental state of the user . Therefore, it should be considered that outcomes of tDCS studies are the result of different affected brain networks that may be involved in attention and movements, among other processes.
Volitional locomotion requires automatic control of movement while the cerebral cortex provides commands that are transmitted by neural projections toward the brainstem and the spinal cord. This control involves predictive motor operations that link activity from the cerebral cortex, cerebellum, basal ganglia and brainstem in order to modify actions at the spinal cord level . In general, this set of structures can be considered to form a motor network that allow voluntary movement.
Different parts of the cerebral cortex participate in the performance of self-initiated movement, like the supplementary motor (SMA), the primary motor (M1) and premotor (PM) areas. It is known that M1 is activated during motor execution. Excitatory effects of M1 have been studied with anodal stimulation , finding that activation of this region is related to higher motor evoked potentials (MEPs) and an increment of force movement on its associated body part area [9, 10]. Moreover, M1 seems to be critical in the early phase of consolidation of motor skills during procedural motor learning , i.e., the implicit skill acquisition through the repeated practice of a task .
In addition, the SMA and PM influence M1 in order to program opportune precise motor commands when movement pattern is modified intentionally, based on information from temporoparietal cortices regarding to the body’s state . The SMA contributes in the generation of anticipatory postural adjustments . Consequently, its facilitatory stimulation seems to increase anticipatory postural adjustments amplitudes, to reduce the time required to perform movements during the learning task of sequential movements, and to produce early initiation of motor responses [14, 15, 16]. These studies suggest the possibility of using SMA excitation during treatments for motor disorders, since hemiparesis after stroke involves the impairment of anticipatory motor control at the affected limb . In addition, some studies propose the participation of the SMA in motor memory and both implicit and explicit motor learning [18, 19, 20, 21], i.e, when new information is acquired without intending to do so and when acquisition of skill is conscious , respectively. Complimentary to the role of SMA, the PM is crucial for sensory-guided movement initiation and the consolidation of motor sequence learning during sleep [8, 23], while its facilitation with anodal tDCS seems to enhance the excitability from the ipsilateral M1 , which may be useful for treatment of PM disorders.
As previously mentioned, the cerebellum is also involved in locomotion through the regulation of motor processes by influencing the cerebral cortex, among other neural structures. During adaptive control of movement, as in the gait process, it seems that loops that interconnect reciprocally motor cortical areas to the basal ganglia and cerebellum allow predictive control of locomotion and they exhibit correlation with movement parameters [8, 25]. Regarding to studies about cerebellar stimulation, there is still not enough knowledge about the effects of tDCS on different neuronal populations and the afferent pathways, so results are variable among studies and their interpretation is more complex than for cerebral tDCS . Furthermore, the topographical motor organization of the cerebellum is not clear yet . Nevertheless, most studies base their experimental procedure on the existence of decussating cerebello-cerebral connections, even if there are also ipsilateral cerebello-cerebral tracts or inter-hemispheric cerebellar connections . Hence, a cerebellar hemisphere is stimulated to affect cerebellar brain inhibition (CBI), which refers to the inherent suppression of cerebellum over the contralateral M1 . For example, the supply of anodal and cathodal stimulation over the right cerebellum in  resulted in incremental and decremental CBI on the left M1, respectively. In contrast, there are some studies that suggest this expectation may be not always appropriate. In  it was shown that inhibitory transcranial magnetic stimulation (a stimulation technique that provides magnetic field pulses on the brain ) over the lateral right cerebellum led to procedural learning decrement for tasks performed with either the right or left hand, whereas inhibition of lateral left cerebellar hemisphere decreased learning only with the left hand. In addition, results from  showed that cathodal cerebellar tDCS worsened locomotor adaptation ipsilaterally. These two studies may provide a reference for using cerebellar inhibition for avoiding undesired brain activity changes during motor rehabilitation, such as compensatory movement habits that might contribute to maladaptative plasticity and hamper the goal of achieving a normal movement pattern . […]
[ARTICLE] Classification of EEG signals for wrist and grip movements using echo state network – Full Text
Brain-Computer Interface (BCI) is a multi-disciplinary emerging technology being used in medical diagnosis and rehabilitation. In this paper, different techniques of classification and feature extraction are applied to analyse and differentiate the wrist and grip flexion and extension for synchronized stimulation using sensory feedback in neuro-rehabilitation of paralyzed persons. We have used an optimized version of Echo State Network (ESN) to identify as well as differentiate the wrist and grip movements. In this work, the classification accuracy obtained is greater than 96% in a single trial and 93% in discrimination of four movements in real and imagination.
The popularity of analysing brain rhythms and its applications in healthcare is evident in rehabilitation engineering. Motor disabilities as a consequence of stroke require rehabilitation process to regain the motor learning and retrieval. The classification of EEG signals obtained by using a low cost Brain Computer Interface (BCI) for wrist and grip movements is used for recovery. Using Movement Related Cortical Potential (MRCP) associated with imaginary movement as detected by the BCI, an external device can be synchronized to provide sensory feedback from electrical stimulation . The timely detection, classification of movement and the real time triggering of the electrical stimulation as a function of brain activity is desirable for neuro-rehabilitation [2,3]. Thus, BCI has an active role in helping out the paralyzed persons who are not able to move their hand or leg . Using BCI system, EEG data is recorded and processed. The acquired data should have the least component of environmental noise and artifacts for effective classification . EEG signals acquired from the invasive method are found to exhibit least noise components and higher amplitude. However, in most applications, a non-invasive method is preferred. The human brain contains a number of neuron networks. EEG provides a measurement of brain activity as voltage fluctuations which are recorded as a result of ionic current within neurons present inside the brain . Many people have motor disabilities due to the nerve system breakdown or accidental failure of nerve system. There are different methods to resolve this problem, e.g. neuro-prosthetics (neural prosthetics) and BCI [3,7–9]. In neuro-prosthetics, a solution of the problem is in the form of connecting brain nerve system with the device and in BCI connecting brain nerve system with computer . BCI produce a communication between brain and computer via EEG, ECOG or MEG signals. These signals contain information of any of our body activity . Moreover, in addition to neuro-rehabilitation, assistive robotics and brain control mobile robots also utilizes similar technologies as reported recently [11,12]. The signal processing of these low amplitude and noisy EEG signals require special care during data acquisition and filtering. After recording EEG measurements, these signals are processed via filtration, feature extraction, and classification. Simple first or second order Chebyshev or Butterworth filter can be used as a low pass, high pass or a notch filter. Some features can be extracted by using one of the techniques from time analysis, frequency analysis, time-frequency analysis or time-space-frequency analysis [13,14]. Extracted EEG signal further classify by using one of the techniques like LDA, QDA, SVM, KNN etc. [15,16].
We aim to classify the wrist and grip movements using EEG signals. This research will be helpful for convalescence of persons having disabilities in wrist or grip. Our work is based on offline data-sets, in which the EEG data is collected multiple times from 4 subjects. We present the following major contributions in this paper: First, the differentiation between the wrist and grip movements has been performed by using imaginary data as well as the real movements. Secondly, we have tested multiple algorithms for feature extraction and classification and used ESN with optimized parameters for best results. This paper is organized as follows: section 2 describes a low-cost BCI setup for EEG, section 3 deals with the DAQ protocol, section 4 explains the echo state network and its optimization while section 5 discusses results obtained in this research. Section 6 concludes the paper.
Brain Computer Interface Design
Brain-Computer Interface (BCI) design requires a multi-disciplinary approach for engineers to observe EEG data. Today, a number of sensing platforms are available which provide a low-cost solution for high-resolution data acquisition. Developing a BCI interface requires a two-step approach namely the acquisition and the real-time processing. In off-line processing, the only requirement is to do the acquisition. The data is acquired via a wireless network from the pick-off electrodes arranged on the scalp of the subjects . One such available system is Emotiv, which is easy to install and use. Emotiv headset with 14 electrodes and 2 reference electrodes, CMD and DRL, is used to collect data as shown in Figure 1. All electrodes have potential with respect to the reference electrode. Emotiv headset is a non-invasive device to collect the EEG data as preferred in most of the diagnosis and rehabilitation applications .
It is important to understand the EEG signal format and frequency content for pre-processing and offline classification. Table 1 shows some of the indications of physical movements and mind actions associated with different brain rhythms in somewhat overlapping frequency bands. It is obvious that the motor imagery tasks are associated with the μ-rhythm in 8-13 Hz frequency band .
|Δ||0-4||Deep sleep stage||Hypoglycaemia, Epilepsy|
|υ||4-7||Initial sleep stage||–|
|α||8-12||Closure of eyes||Migraine, Dementia|
|β||12-30||Busy/Anxious thinking||Encephalopathies, Tonic seizures|
|µ||8-13||Motor imagery tasks||Autism Spectrum Disorder|
Table 1. Brain frequency bands and their significance.
[Abstract] BCI controlled neuromuscular electrical stimulation enables sustained motor recovery in chronic stroke victims – PDF
R. Leeb1,2,#, A. Biasiucci2,#, T. Schmidlin1 , T. Corbet2 , P. Vuadens3 , JdR. Millán2,*
- Center for Neuroprosthetics (CNP), École Polytechnique Fédérale de Lausanne, Sion, Switzerland;
- Chair in Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne, Geneva, Switzerland;
- SUVACare – Clinique Romande de Réadaptation, Sion, Switzerland
Equal contributions; * Campus Biotech, Chemin des Mines 9, CH-1202 Geneva, Switzerland; E-mail: firstname.lastname@example.org
Introduction: Recently, it has been shown that brain-computer interfaces (BCI) can be used in stroke rehabilitation to decode motor attempts from brain signals and to trigger movements of the paralyzed limb . Among other available practices in rehabilitation, neuromuscular electrical stimulation (NMES) is often used to directly engage muscles on the affected parts of the body during physical therapy. Nevertheless, the benefits of a combined approach, to directly link the brain intention with a muscular response, are not yet fully validated. In this abstract, we report first results of a BCI-NMES system for stroke rehabilitation.
Material and Methods: Up to now, we enrolled 18 chronic stroke victims (minimum 10 months past the incident) suffering from an impairment of the upper limb in a randomized controlled clinical trial. Half of the subjects were assigned to the BCI group and half to a “sham” group, whereby the criteria such as motor impairment –measured via the Fugl-Meyer scale for upper extremity (FM) score–, age, time since incident and lesion location were balanced. Generally, the experimental protocol consisted of three different phases: (i) patients underwent a preevaluation to check the motor capabilities, to characterize the initial state of the brain and to calibrate the BCI classifier (see BCI details in ). (ii) In the following weeks, they were trained with an online BCI twice a week for 10 sessions (45 to 90 minutes including setup). (iii) Finally, they performed a post-experimental screening to determine changes in EEG patterns and in motor functions following the treatment, and a 6-month follow-up to evaluate the sustainment. Patients in the BCI group received NMES of the extensor digitorum muscles triggered by the BCI detecting the intention of movement at the cortical level (modulation of the sensorimotor rhythm in the contralateral motor cortex). For patients in the sham group the NMES was not correlated with the brain activity. All subjects were asked to attempt to open their paretic hand (full sustained finger extension) with the aim of activating the NMES upon detection of a suitable sensorimotor rhythms (Fig. 1-a). Subjects in the two groups (BCI and sham) received comparable amount of NMES.
Results: Remarkably, subjects in the BCI group improved their motor function (post minus pre) by 8.6±5.0 FM points (which is more than the minimal clinical change of 5.25 FM points), while those in the sham group improved only by 2.4±3.4 FM points (Fig. 1-b). As expected, the features used by the BCI classifier were mostly located over the affected hemisphere and the motor cortex (see topographic presentation in Fig. 1-c).
Discussion: We hypothesize that the motor improvement in the BCI group (in contrast to the sham group) is triggered by the tight timed and functional link between the intended action in the brain, and the executed and perceived motor action, through the activation of the body’s natural efferent and afferent pathways.
Significance: In our randomized controlled trial, we demonstrate that the modulation of sensorimotor rhythms driving contingent neuromuscular stimulation is more effective than sham stimulation with active motor attempt, and that the proposed therapy dosage produces a clinically important recovery in chronic stroke survivors having a moderate-to-severe motor impairment.
References:  Ramos-Murguialday A, et al. Brain-machine interface in chronic stroke rehabilitation. Ann Neurol, 74(1):100-108, 2013.  Leeb R, et al. Transferring brain-computer interfaces beyond the laboratory: Successful application control for motor-disabled users. Artif Intell Med, 59: 121-132, 2013.
This talk was given at the BCI Meeting 2016 at Asilomar Conference Grounds on May 31st, 2016.
[Abstract] Brain-computer interfaces for communication and rehabilitation : Nature Reviews Neurology
Brain–computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.
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[ARTICLE] Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis – Full Text
The use of Brain–Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain’s capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.
In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence.
Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience.
Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user’s profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.
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