[Abstract] A motor rehabilitation BCI with multi-modal feedback in chronic stroke patients (P5.300)

ABSTRACT

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.

Source: A motor rehabilitation BCI with multi-modal feedback in chronic stroke patients (P5.300)

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