A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals

Abstract

The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user’s intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.

TSTELM: Two-Stage Transfer Extreme Learning Machine for Unsupervised Domain Adaptation

“…Support vector machine (SVM) [ 1 ], k-nearest neighbor (kNN) [ 2 ], naive Bayes [ 3 , 4 ], decision tree [ 5 ], logistic regression [ 6 ], and many other classifiers with high accuracy appear and have attracted much attention. Huang et al [ 7 ] proposed extreme learning machine (ELM) which is a better classifier with powerful nonlinear fitting and approximation capabilities [ 8 , 9 ] and has been widely studied and applied in brain-computer interfaces [ 10 , 11 ], medical diagnosis [ 12 , 13 ], fault diagnosis [ 14 ], hyperspectral [ 15 ], and other fields.…”

An Adaptive EEG Classification Algorithm Based on CSSD and ELM_Kernel for Small Training Samples

“…Gu and Hua proposed a fusion feature that combined temporal and spatial features as the final feature data. The fusion features were input to the trained ELM classifier, and the ELM model achieved a better classification accuracy [ 31 ]. Extreme learning machine with kernel (ELM_Kernel) algorithm introduced the kernel function into the ELM algorithm can obtain the minimum square optimization solutions.…”

F-Value Time-Frequency Analysis: Between-Within Variance Analysis

“…The analysis can be used to investigate the cause of diverse diseases or diagnose it ( Dubreuil-Vall et al, 2020 ; Sebastian-Romagosa et al, 2020 ; Lin et al, 2021 ). Moreover, it is the core technology in the brain-computer interface (BCI), which controls computers or various electronic devices according to various intentions ( Gu and Hua, 2021 ; Panachakel and Ramakrishnan, 2021 ). If the characteristics of neural signals change a little under the same condition and a lot among different conditions, the characteristics reflect the conditional changes.…”