EEG Signal Analysis Based Brain-Computer

Nooreldeen, Hend; Badawy, Samir; El-Brawany, Mohamed A

doi:10.21608/mjeer.2021.193083

	EEG Signal Analysis Based Brain-Computer
Menoufia Journal of Electronic Engineering Research
Article 5, Volume 30, Issue 2, July 2021, Page 34-38 PDF (461.42 K)
Document Type: Original Article
DOI: 10.21608/mjeer.2021.193083
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Authors
Hend Nooreldeen^* ; Samir Badawy; Mohamed A El-Brawany
Department of Industrial Electronics and Control Engineering , Faculty of Electronic Engineering, Menoufia University, Egypt.
Abstract
EEG Signal Analysis Based Brain-Computer
Highlights
Results of this paper prove that imagination of right and left hand movement may alter activity of neural in primary sensorimotor regions, resulting in the changes of the μ and b rhythms. Moreover, the results show that the distinction between electrode c4and c3 is more clear in case of right hand imagery. The results of this work can be used as a base in the classification stage for BCI and device control. Work in the future will use the results of this study to directly extract quantitative features to characterize between right and left -hand imagery and monitor embedded rehabilitation robot to assist patients with extreme paralysis in controlling their environmental and communicating with the outside world.
Keywords
Brain-computer interface (BCI); Electroencephalography (EEG) signals; motor imagery EEG(MI-EEG)


Full Text
EEG signals are measure for brain neural activity, which change according to task performed by a person [1]. The EEG has several medical applications. For example, EEG has been used as a brain diseases diagnostic method [2] Also, neuroscience advances, brain imaging technologies and computing have provided us with the opportunity to directly interface the brain with a computer, thereby creating a control and communication system that bypasses peripheral muscles and nerves to enable interaction through brain activity alone , this is known as brain-computer interface[3][4][5] . BCI depends on voluntarily modifying user mental state to control a device whereas a pattern analysis method concurrently tries to determine the parallel change of EEG signals[6]. fig. 1shows the common structure for BCI system. acquired brain signals at electrodes located on the scalp or in the head are processed to obtain determined signal features .These features are converted to control signals to control machines. EEG methods commonly executed for BCIs depend on activity of sensorimotor rhythms (SMR) measured during movement imagery and permit establishing sensorimotor rhythm-based BCI (SMR BCI)or motor-imagery BCI (MI BCI) [7]. experimentally MI-BCI can control devices like wheelchairs[8]. Wolpaw and McFarland [9] showed that depending on imagination of left and right hand movement a cursor two-dimensional control was possible. The work was increased to a cursor three-dimensional control depending on imagination of foot, left, and right hand movement[10]. The introduction of four-class BCIs enabled users to fly a virtual helicopter[11] and a robotic quad-copter in three dimensions [12]. imagining moving and relax the two hands made the helicopter fly forward and reverse and imagining moving left and right hand made the helicopter turn around left and right. The work in [11] was extended in [13] to a six-class BCI. feet and tongue movement imagery made the exceed dimensional control. Previous mentioned studies depend on more than two electrodes . As shown in [7], handedness has an influence on sensorimotor rhythm (SMR) distribution and BCI function. During imagery of left-hand movements, left-handers have lower precision and poorer SMR suppression in the alpha band (8–13 Hz). similar results have been observed in our work where the difference is clearer in case of right than left hand imagery. In [14] quantitative EEG changes were extracted from various combinations of channels due to movement imagination. To determine frequency bands specific to the subject, a characteristic filter bank popular spatial algorithm was introduced in [15] to show how EEG activity changes with left and right hand imagery movements depending on two channels and this require further computation. In this paper, we show a study of how EEG activity changes with imagery of left and right hand movements depending on only two electrodes as a step toward developing BCI based assistive devices and toward portable BCI. The analysis techniques implemented in our work include power spectrum, event related potentials and the EEG signal time frequency analysis. Those analysis techniques successfully facilitate classification between imagination of left and right hand movements in a multidimension way depending on two electrodes. The result showed that we can classify right and left hand imagery movements in either frequency, time , or combined timefrequency domain. Fig.1. Architecture of an EEG-based BCI Remainder of paper is arranged in the following manner: Section II explains methods and materials used. Section III introduces results and its discussion. The conclusions and future work are presented in section IV.
References
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