OPTIMIZATION OF PRE-PROCESSING ROUTINES IN SPEECH IMAGERY-BASED EEG SIGNALS

被引:1
|
作者
Sree, R. Anandha [1 ]
Kavitha, A. [1 ]
Divya, B. [1 ]
机构
[1] Sri Sivasubramaniya Nadar Coll Engn, Ctr Healthcare Technol, Dept Biomed Engn, Chennai 603110, Tamil Nadu, India
关键词
Speech imagery; electroencephalography (EEG); signal-to-noise ratio (SNR); mean square error (MSE); peak signal-to-noise ratio (PSNR);
D O I
10.1142/S0219519423400328
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
Speech imagery is one type of mental imagery specific to processing verbal sequences and plays a vital role in human thought processes. Speech imagery has become an interesting paradigm for researchers as speech imagery has a high similarity to real voice communication. Electroencephalography (EEG) is a noninvasive electrophysiological technique that measures the mental state of the brain directly from the scalp. The nature of the acquired EEG signals is nonlinear and nonstationary. As EEG signals have a low signal-to-noise ratio (SNR), artifacts occur during acquisition. Hence, an efficient framework of pre-processing is required to obtain artifact-free EEG for further applications. Selection of the optimal pre-processing techniques for EEG still remains a challenging task. This work focuses on employing and comparing the different pre-processing techniques and lists out the optimal solutions for pre-processing Speech imagery-based EEG signals. The techniques are compared based on the Mean Square Error and Peak Signal-to-Noise Ratio values.
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收藏
页数:9
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