Posts Tagged Brain modeling

[Abstract] Focal onset seizure prediction using convolutional networks

Abstract:

Objective: This work investigates the hypothesis that focal seizures can be predicted using scalp electroencephalogram (EEG) data. Our first aim is to learn features that distinguish between the interictal and preictal regions. The second aim is to define a prediction horizon in which the prediction is as accurate and as early as possible, clearly two competing objectives.
Methods: Convolutional filters on the wavelet transformation of the EEG signal are used to define and learn quantitative signatures for each period: interictal, preictal, and ictal. The optimal seizure prediction horizon is also learned from the data as opposed to making an a priori assumption.
Results: Computational solutions to the optimization problem indicate a ten-minute seizure prediction horizon. This result is verified by measuring Kullback-Leibler divergence on the distributions of the automatically extracted features.
Conclusions: The results on the EEG database of 204 recordings demonstrate that
  1. the preictal phase transition occurs approximately ten minutes before seizure onset, and
  2. (the prediction results on the test set are promising, with a sensitivity of 87.8% and a low false prediction rate of 0.142 FP/h.
Our results significantly outperform a random predictor and other seizure prediction algorithms.
Significance: We demonstrate that a robust set of features can be learned from scalp EEG that characterize the preictal state of focal seizures.

via Focal onset seizure prediction using convolutional networks – IEEE Journals & Magazine

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[Abstract] A motion rehabilitation self-training and evaluation system using Kinect

Abstract:

Stroke patients usually have difficulties to conduct rehabilitation training themselves, due to no rehabilitation evaluation in time and dependence on doctors. In order to solve this problem, this paper proposes a motion rehabilitation and evaluation system based on the Kinect gesture measuring technology combining VR technology as well as traditional method of stroke rehabilitation. Real-time rehabilitation motion feedback is achieved by using Kinect motion capturing, customized skeleton modeling, and virtual characters constructed in Unity3D. The jitter problem of virtual characters following motion using Kinect is solved. Fidelity and interactivity of virtual rehabilitation training is improved. Our experiment validated the feasibility of this system preliminarily.

1. Introduction

With an ageing population problem increasingly prominent, the number of hemiplegia patients is growing caused by stroke, which has a high morbidity and high mortality rate [1]. Stroke can lead to the dysfunction of the brain central nervous, often characterized by language, cognitive or motor dysfunction [2], [3]. The medical rehabilitation mechanism of stroke is based on neural plasticity theory and the theory of mirror neurons [4].

Source: A motion rehabilitation self-training and evaluation system using Kinect – IEEE Xplore Document

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[Abstract] Fatigue detection and estimation using auto-regression analysis in EEG

Abstract:

Estimation of fatigue is a required criteria in the field of physiology. The estimation of muscle fatigue and its development in the brain signals can provide a level of endurance among athletes and limits of a persons in doing physical tasks. In this paper a technique for detecting and estimating the fatigue development using regression parameters for EEG signals is discussed. The study of 14 subjects was undertaken and analysed for the fatigue development using Auto-Regression(AR) model. The behaviour of the error function obtained is analysed for the prediction of the stages and limits of muscle fatigue development.

I. Introduction

Muscle fatigue is a phenomenon associated with the muscle contraction. It is understood as the reduction in the ability of maximal force generation by the muscle with time, during its stressing, as the muscle contraction keeps on increasing. The nervous system’s limitation to generate sustainable signals and the reduction of ability of muscle fiber to contract are two major factors contributing to fatigue development [1]. Fatigue development limits the performance and capability of the individual in sports, long stretch driving conditions and in rigourous day to day activities. Hence a parameter that can estimate the fatigue levels and provide a break point for maximum fatigue can be useful for physiology and in other areas such as labour. People working under mines can be monitored for the fatigue break point and the overall productivity of such areas can be increased by proper analysis. The fatigue development in a person can be analysed via number of methods based on physiological changes. These include Electroencephalogram (EEG), Elec-tromyography(EMG), and Heart Rate Variability(HRV). Zadry et.al. [2] reported the increase in alpha band power level of EEG with time for fatigue development [3]. Ali et.al. also reported increase in RMS values of different bands in EEG [4]. Few studies measure brain activity in light repetitive task using EEG [5] to measure drowsiness or fatigue on drivers [6] [7] and night work [8] [9]. The EEG analysis for overall fatigue has been the focus of research, but research for specific muscle fatigue detection has been limited. The EEG based detection of fatigue has the advantage of quantitative based assessment. But, for real time application perspective faster computational power and signal processing methods are required. One of the challenges based on EEG based approach is the disturbances and contamination of the signal from eyes blinking action, muscle noise by movements and instrumental noises like line noise, electronic interferences [10]. Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness [11].

Source: Fatigue detection and estimation using auto-regression analysis in EEG – IEEE Xplore Document

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