Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography

Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing...

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主要な著者:	Sehyeon Kim, Dae Youp Shin, Taekyung Kim, Sangsook Lee, Jung Keun Hyun, Sung-Min Park
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2022-01-01
シリーズ:	Sensors
主題:	brain–computer interface (BCI) convolutional neural network (CNN) electroencephalography (EEG) electromyography (EMG) transforearm amputees transfer learning (TL)
オンライン･アクセス:	https://www.mdpi.com/1424-8220/22/2/680
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