High Classification Accuracy of a Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation Training

OBJECTIVES/SPECIFIC AIMS: The objective of this study is to determine the degree to which the use of a contralesionally-controlled brain-computer interface for stroke rehabilitation drives change in interhemispheric motor cortical activity. METHODS/STUDY POPULATION: Ten chronic stroke patients were trained in the use of a brain-computer interface device for stroke recovery. Patients perform motor imagery to control the opening and closing of a motorized hand orthosis. This device was sent home with patients for 12 weeks, and patients were asked to use the device 1 hour per day, 5 days per week. The Action Research Arm Test (ARAT) was performed at 2-week intervals to assess motor function improvement. Before the active motor imagery task, patients were asked to quietly rest for 90 seconds before the task to calibrate recording equipment. EEG signals were acquired from 2 electrodes—one each centered over left and right primary motor cortex. Signals were preprocessed with a 60 Hz notch filter for environmental noise and referenced to the common average. Power envelopes for 1 Hz frequency bands (1–30 Hz) were calculated through Gabor wavelet convolution. Correlations between electrodes were then calculated for each frequency envelope on the first and last 5 runs, thus generating one correlation value per subject, per run. The chosen runs approximately correspond to the first and last week of device usage. These correlations were Fisher Z-transformed for comparison. The first and last 5 run correlations were averaged separately to estimate baseline and final correlation values. A difference was then calculated between these averages to determine correlation change for each frequency. The relationship between beta-band correlation changes (13–30 Hz) and the change in ARAT score was determined by calculating a Pearson correlation. RESULTS/ANTICIPATED RESULTS: Beta-band inter-electrode correlations tended to decrease more in patients achieving greater motor recovery (Pearson’s r=−0.68, p=0.031). A similar but less dramatic effect was observed with alpha-band (8–12 Hz) correlation changes (Pearson’s r=−0.42, p=0.22). DISCUSSION/SIGNIFICANCE OF IMPACT: The negative correlation between inter-electrode power envelope correlations in the beta frequency band and motor recovery indicates that activity in the motor cortex on each hemisphere may become more independent during recovery. The role of the unaffected hemisphere in stroke recovery is currently under debate; there is conflicting evidence regarding whether it supports or inhibits the lesioned hemisphere. These findings may support the notion of interhemispheric inhibition, as we observe less in common between activity in the 2 hemispheres in patients successfully achieving recovery. Future neuroimaging studies with greater spatial resolution than available with EEG will shed further light on changes in interhemispheric communication that occur during stroke rehabilitation.

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Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3904 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2918-2927

Author(s):

A. Shankar ◽

S. Muttan ◽

D. Vaithiyanathan

Keyword(s):

Neural Network ◽

Signal Processing ◽

Motor Imagery ◽

Classification Accuracy ◽

Brain Computer Interface ◽

Computer Interface ◽

Discrete Wavelet ◽

Feature Sets ◽

Evaluation Of Performance ◽

The Brain

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

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Effect of extensive training load on the classification accuracy for a three class motor imagery based brain-computer interface

2016 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA) ◽

10.1109/actea.2016.7560141 ◽

2016 ◽

Author(s):

M. H. Zaky ◽

M. E. Khedr ◽

A. A. Nasser

Keyword(s):

Motor Imagery ◽

Classification Accuracy ◽

Brain Computer Interface ◽

Training Load ◽

Computer Interface ◽

Extensive Training

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Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

Discrete Dynamics in Nature and Society ◽

10.1155/2011/724697 ◽

2011 ◽

Vol 2011 ◽

pp. 1-8 ◽

Cited By ~ 22

Author(s):

Chu Kiong Loo ◽

Andrews Samraj ◽

Gin Chong Lee

Keyword(s):

Feature Extraction ◽

Fractal Dimension ◽

Motor Imagery ◽

Classification Accuracy ◽

Brain Activity ◽

Brain Computer Interface ◽

Computer Interface ◽

Estimation Methods ◽

Support Vector ◽

Linear Discriminant

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

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Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery

10.21203/rs.3.rs-89367/v1 ◽

2020 ◽

Author(s):

Lujia Zhou ◽

Xuewen Tao ◽

Feng He ◽

Peng Zhou ◽

Hongzhi Qi

Keyword(s):

Steady State ◽

Motor Imagery ◽

Stroke Rehabilitation ◽

Evoked Potential ◽

Brain Computer Interface ◽

Recognition Rate ◽

Computer Interface ◽

Task Specificity ◽

Rehabilitation Technology ◽

The Brain

Abstract Background: In recent years, the brain-computer interface (BCI) based on motor imagery (MI) has been considered as a potential post-stroke rehabilitation technology. However, the recognition of MI relies on the event-related desynchronization (ERD) feature, which has poor task specificity. Further, there is the problem of false triggering (irrelevant mental activities recognized as the MI of the target limb). Methods: In this paper, we discuss the feasibility of reducing the false triggering rate using a novel paradigm, in which the steady-state somatosensory evoked potential (SSSEP) is combined with the MI (MI-SSSEP). Data from the target (right hand MI) and nontarget task (rest) were used to establish the recognition model, and three kinds of interference tasks were used to test the false triggering performance. In the MI-SSSEP paradigm, ERD and SSSEP features modulated by MI could be used for recognition, while in the MI paradigm, only ERD features could be used. Results: The results showed that the false triggering rate of interference tasks with SSSEP features was reduced to 29.3%, which was far lower than the 55.5% seen under the MI paradigm with ERD features. Moreover, in the MI-SSSEP paradigm, the recognition rate of the target and nontarget task was also significantly improved. Further analysis showed that the specificity of SSSEP was significantly higher than that of ERD (p<0.05), but the sensitivity was not significantly different. Conclusions: These results indicated that SSSEP modulated by MI could more specifically decode the target task MI, and thereby may have potential in achieving more accurate rehabilitation training.

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