A Hybrid Brain-Computer Interface using Extreme Learning Machines for Motor Intention Detection

Autor:	Oluwarotimi Williams Samuel, Alberto López Delis, Denis Delisle Rodriguez, Ramón Osmany Ramírez Tasé
Rok vydání:	2021
Předmět:	medicine.diagnostic_test Computer science business.industry Interface (computing) Bayesian probability Feature extraction Pattern recognition Electroencephalography Zero crossing Autoregressive model Frequency domain medicine False positive rate Artificial intelligence business
Zdroj:	Progress in Artificial Intelligence and Pattern Recognition ISBN: 9783030896904 IWAIPR
DOI:	10.1007/978-3-030-89691-1_12
Popis:	This work proposes a hybrid brain-computer interface (HBCI) using Bayesian method to fuse information from electroencephalography (EEG) and electromyography signals (EMG) for decoding movement intention. For EEG signal feature extraction, Riemannian covariance matrices are computed, whereas other three features into the time and frequency domain are extracted from each EMG channel, such as autoregressive models, signal slope changes, and zero crossing. In this approach, the Bayesian fusion method is used to combine predictions from Extreme Learning Machines (ELMs) classifiers. Our proposed HBCI obtained average accuracy of 96.26%, recall of 93.97, kappa of 0.78, and false positive rate of 3.31%, outperforming solution only based on EEG or EMG, suggesting its potential for neuro-rehabilitation applications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b1d62613dfd9e70982eec53411c57ed7 https://doi.org/10.1007/978-3-030-89691-1_12 Zobrazit plný text záznamu