Novel Motor Imagery-Based Brain Switch for Patients With Amyotrophic Lateral Sclerosis: A Case Study Using Two-Channel Electroencephalography

• Published in: IEEE consumer electronics magazine Vol. 8; no. 2; pp. 72 - 77
• Main Authors: Liu, Yi-Hung, Huang, Shiuan, Huang, Hao-Chuan, Peng, Wei-Hao
• Format: Magazine Article
• Language: English
• Published: Piscataway IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amyotrophic lateral sclerosis; Brain; Channels; Detectors; Electroencephalography; Emulation; Feature extraction; Image classification; Motors; Patients; Support vector machines; Switches; Testing; Training
• ISSN: 2162-2248; 2162-2256
• DOI: 10.1109/MCE.2018.2880828

This article presents a brain switch for patients with amyotrophic lateral sclerosis (ALS). The firststage extract sample entropy (SampEn) features are from 30-channel electroencephalography (EEG) signals. Fisher's separability criterion is then performed in the second stage to select the top two EEG channels for each patient. Features of the top two channels are sent into the last stage for classification, where a motor imagery detector based on an adaptive support vector machine (ASVM) is used to keep the false positive rate (FPR) below a near-zero threshold and gain a high true positive rate (TPR). The results on five patients with ALS demonstrated the superior performance of the brain switch in both validation and follow-up sessions.