A Comparison Study on Machine Learning Algorithms Utilized in P300- based BCI

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.

Investigation of a wavelet-based neural network learning algorithm applied to P300 based brain-computer interface

“…the inter stimulus interval was 400 ms. The duration of one run was approximately one minute and the duration of one session including setup of electrodes and short breaks between runs was approximately 30 min [3].…”

“…at the beginning of the intensification of an image, and ended 1000 ms after stimulus onset. The fact that P300 Evoked Related Potential (ERP) appears about 300 ms after the stimulus makes this window large enough to capture the required time features for an efficient classification [3].…”

“…Target signals issued from the patient should be translated into commands [3]. The signals can be acquired by collecting brain signals (EEG) [4].…”

“…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 [3]. P300 waves are evoked potentials that are elicited in response to specific stimuli, while SCPs occupy the lowest frequency range of the EEG signal and are associated with cortical activation and deactivation [3].…”

An alternative respiratory sounds classification system utilizing artificial neural networks

“…In contrast, modern computer technology offers great advantages in terms of acquisition, storage, and analysis of biosignals and provides the possibility to automate the process. [4][5][6][7][8][9][10] Computerized lung sound analysis involves recording the patient lung sound via an electronic device, followed by computer analysis and classification of lung sounds based on specific signal characteristics. [2,11] Gurung et al conducted a review and an in-depth analysis of relevant classification studies using a computerized lung sound analysis in which the overall sensitivity and specificity of implemented algorithms were estimated.…”