Abstract
Brain-computer interface (BCI) has extensively been used for rehabilitation purposes. Being in the research phase, the brainwave based wheelchair controlled systems suffer from several limitations, e.g., lack of focus on mental activity, complexity in neural behavior in different conditions, and lower accuracy. Being sensitive to the color stimuli, the EEG signal changes promises a better detection. Utilizing the Electroencephalogram (EEG changes due to different color stimuli, a methodology of wheelchair controlled by brainwaves has been presented in this study. Red, Green, Blue (primary colors) and Yellow (secondary color) were chosen as the color stimuli and utilized in a 2 × 2 color window for four-direction command, namely left and right, forward and stop. Alpha, beta, delta and theta EEG rhythms were analyzed, time and frequency domain features were extracted to find the most influential rhythm and accurate classification model. Four classifiers, namely, K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest Classifier (RFC) and Artificial Neural Networks (ANN) were trained and tested for assessing the performance of each of the EEG rhythm, with a five-fold cross-validation. Four different performance measures, i.e. sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were utilized to examine the wholescale performance. The results suggested that Beta EEG rhythm performs the best apart from all the rhythms for the color stimuli based wheelchair control. While comparing the performance of the classifiers, ANN-based classifier shows the best accuracy of 82.5%, which is higher than the performance of the three other classifiers.
Highlights
This paper is an extension of work originally presented in 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019 [1]
This paper examines the utility of the different color stimulus on the EEG based wheelchair control system
In order to develop EEG controlled user-friendly wheelchair, using this proposed model, an analysis was done in this study to find out the feasibility of the time and frequency domain features
Summary
This paper is an extension of work originally presented in 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019 [1]. The presented paper [1] utilized electroencephalogram (EEG) for wheelchair control using color stimuli where the current article is expanded further to validate the EEG based wheelchair control system using multiple machine learning models. This paper examines the utility of the different color stimulus on the EEG based wheelchair control system. EEG is a reflection of our neurons activity which is associated with all kind of human behaviours- thoughts, emotional state, eye vision etc. EEG changes its value of features with respect to different influencer like eye vision. The EEG rhythms are defined by their frequency range, named delta, theta, alpha, beta and gamma corresponds to 1-4 Hz, 4-8 Hz, 8-13 Hz, 13-30 Hz and 36-44 Hz respectively
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