Binary Classification on ECoG Signals Using Optimized Extremely Learning Machine

Abstract

Abstract. In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left hand little finger or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with p-value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing non-stationarity of ECoG signal.

Binary Models for Motor-Imagery Brain-Computer Interfaces: Sparse Random Projection and Binarized SVM

“…Interestingly, the weights in the matrix of random projection do not need to be stored (i.e., can be rematerialzed by a random function on the fly), or can be realized by emerging memristor [14] and optical [15] devices. A readout function layer can then effectively analyze the projected features for various classification tasks, e.g., in EEG [16], electrocardiography (ECG) signals [17], [18], and electrocorticography (ECoG) [19].…”

Binarization Methods for Motor-Imagery Brain–Computer Interface Classification

“…Interestingly, the weights in the matrix of random projection do not need to be stored (i.e., can be rematerialized by a random function on the fly), or can be realized by emerging memristor [16], [17], [18], [19], [20] and optical [21] devices. A readout function layer can then effectively analyze the projected features for various classification tasks, e.g., in EEG [22], [23], electrocardiography (ECG) signals [24], [25], and electrocorticography (ECoG) [26]. On the other hand, for the CNN-based approaches in MI-BCIs, quantization methods to 8-bit fixed-point weights and activations are developed [27], but having a CNN model with full, or partial, binary weights is still missing in MI-BCIs.…”