Abstract
This study addresses Brain-Computer Interface (BCI) systems meant to permit communication for those who are severely locked-in. The current study attempts to evaluate and compare the efficiency of different translating algorithms. The setup used in this study detects the elicited P300 evoked potential in response to six different stimuli. Performance is evaluated in terms of error rates, bit-rates and runtimes for four different translating algorithms; Bayesian Linear Disciminant Analysis (BLDA), Linear Discriminant Analysis (LDA), Perceptron Batch (PB), and nonlinear Support Vector Machines (SVMs) were used to train the classifier whilst an N-fold cross validation procedure was used to test each algorithm. A communication channel based on Electroencephalography (EEG) is made possible using various machine learning algorithms and advanced pattern recognition techniques. All algorithms converged to 100% accuracy for seven of the eight subjects. While all methods obtained fairly good results, BLDA and PB were superior in terms of runtimes, where the average runtimes for BLDA and PB were 13 ± 2 and 15.6 ± 6 seconds, respectively. In terms of bit-rates, BLDA obtained the highest average value (22 ± 12 bits/minute), where the average bit-rate for all subjects, all sessions, and all algorithms was 18.76 ± 10 bits/minute.
Highlights
The electrical activity of the brain i.e., electroencephalography (EEG) originates mainly from the cerebral cortex
After an extensive first session of supervised algorithm learning and feedback, a study based on the datasets provided by Brain-Computer Interface (BCI) competition 3 incorporated the use of adaptive linear discriminant analysis (ALDA) for classification of different motor imageries [22]
People suffering from neuromuscular dysfunction may use a P300based BCI to communicate with their environment quite successfully
Summary
The electrical activity of the brain i.e., electroencephalography (EEG) originates mainly from the cerebral cortex. There are currently several major categories of BCIs in use that are classified based on the type of neurophysiologic signal they utilize These categories include, but are not limited to, Visual Evoked Potentials (VEPs), P300 elicitation, alpha and beta rhythm activity, slow cortical potentials (SCPs), and microelectrode cortical neuronal recordings [2,3,11]. After an extensive first session of supervised algorithm learning and feedback, a study based on the datasets provided by BCI competition 3 incorporated the use of adaptive linear discriminant analysis (ALDA) for classification of different motor imageries [22]. It attempts to evaluate the performance of four different translating algorithms (BLDA, LDA, PB, and Nonlinear SVM)
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