Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

Version 1 : Received: 31 May 2021 / Approved: 1 June 2021 / Online: 1 June 2021 (10:04:49 CEST)
Version 2 : Received: 30 December 2021 / Approved: 4 January 2022 / Online: 4 January 2022 (17:56:46 CET)

How to cite: Rușanu, O.A. A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor . Preprints 2021, 2021060016 (doi: 10.20944/preprints202106.0016.v2). Rușanu, O.A. A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor . Preprints 2021, 2021060016 (doi: 10.20944/preprints202106.0016.v2).

Abstract

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

Keywords

brain-computer interface; EEG signal; artificial neural networks, LabVIEW application; features extraction; eye-blinks detection; EEG headset

Subject

ENGINEERING, Biomedical & Chemical Engineering

Comments (1)

Comment 1
Received: 4 January 2022
Commenter: Oana Andreea Rușanu
Commenter's Conflict of Interests: Author
Comment: Extended version of the same paper
Additional Information and Details to the Sections: Introduction, Results, Discussion
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