EEG Signal Processing for Brain–Computer Interfaces

Georgieva, Pétia; Silva, Filipe; Milanova, Mariofanna; Kasabov, Nikola

doi:10.1007/978-3-642-30574-0_46

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…The highfrequency bands, e.g., beta or gamma, are dominated by muscular artefacts. Even though these waves correspond to the concentration or active attention [53], thus, they could have information for imagined speech recognition. Meanwhile, the low-frequency bands do not correlate with concentration [54].…”

Section: Spectral Spatial and Temporal Analysismentioning

confidence: 99%

EEG-Based Silent Speech Interface and its Challenges: A Survey

Fitriah¹,

Zakaria²,

Rajab³

2022

IJACSA

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People with speech disorders could have social and welfare difficulties. Therefore, the silent speech interface (SSI) is needed to help them communicate. This interface decodes the speech from a human's biosignal. The brain signals contain information from speech production to cover people with numerous speech disorders. Brain signals can be acquired noninvasively by electroencephalograph (EEG) and later transformed into the features for the input of speech pattern recognition. This review discusses the advancement of EEGbased SSI research and its current challenges. It mainly discussed the acquisition protocol, spectral-spatial-temporal characterization of EEG-based imagined speech, classification techniques with leave-one-subject or session-out cross-validation, and related real-world environmental issues. It aims to aid future imagined speech decoding research in exploring the proper methods to overcome the problems.

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Section: Spectral Spatial and Temporal Analysismentioning

confidence: 99%

EEG-Based Silent Speech Interface and its Challenges: A Survey

Fitriah¹,

Zakaria²,

Rajab³

2022

IJACSA

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“…They are associated with focused concentration, attention, and arousal. Gamma waves which range from frequency 30 Hz and up are associated with perception and cognition (Georgieva et al, 2014).…”

Section: Brain Control: Eeg Processingmentioning

confidence: 99%

Fuzzy Logic Based Eye-Brain Controlled Web Access System

Mirchandani¹

2015

CIS

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Accessing the Web is crucially important in today's society because of communication, education, and entertainment opportunities. Paralyzed or paretic individuals are unable to capitalize on these opportunities using traditional human-computer interaction methods. Thus, I develop a low-cost web browsing system for such individuals, integrating eye and brain control in a novel fashion to relay and interpret navigation commands. The system combines gaze position estimates obtained from a new image processing algorithm with brain concentration levels sensed and transmitted by an electroencephalogram headset. Since user intent may itself be uncertain, the system incorporates a novel fuzzy logic algorithm for combining brainwave and eye position inputs to determine the user's targeted hyperlink. The algorithm adopts an approach based on exponential smoothing to efficiently keep a record of historical signals. Experimental evaluation established that the first attempt success rate of the system lies between 87% and 95% with 95% confidence. Error recovery accuracy is 98.4%, resulting in a second attempt success rate of 99.1%. This assistive technology, using off-the-shelf components, costs less than $100 excluding the tablet computer, and has the potential to affordably open up the Web to millions of individuals.

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“…Neurons of the human brain are considered as structural subunits in the complex hierarchal brain structure [1,2]. Brain refers to the command center of the human body that is controlled by the interconnected network of neurons [3]. Any psychophysiological change in the human body is directly related to the change in the cortical neuroelectric potential.…”

Section: Introductionmentioning

confidence: 99%

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

Saha

Rahman²,

Alam

et al. 2021

SN COMPUT. SCI.

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The extraction methodology of the significant features from the signals is one of the most important prerequisite steps for EEG signal classification. Common spatial pattern (CSP) is a widely used feature extraction method for EEG signal but with a lacking of failing to maintain discriminative features between classes in the time domain, and further as a consequence, ends up in inconvenience with erroneous output. To overcome the limitations of the convention CSP, this research work proposes a novel frequency domain CSP (FCSP) method for feature extraction. This method proposes to convert the time domain EEG signal to its power spectral density (PSD) so that the event-related variation can be found in the frequency domain. After that, the CSP method is applied to the PSD values of the selected channels to extract the variation based on the spatial pattern of the channels for the events. The output of this method helps to extract simple features from the FCSP-PSD data for the classification. The proposed method is applied to motor imagery data from BCI competition IV. To check the applicability of the proposed method, a complex environment was created considering the same lobe events such as combined left and right feet (Class#1) versus right-hand (Class#2) imagery movement. To compare the performance of the proposed work, the method is also applied to the conventional classification problem (left-hand vs right-hand imagery movement) and found very promising results of 91% accuracy on average.

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EEG Signal Processing for Brain–Computer Interfaces

Cited by 15 publications

References 26 publications

EEG-Based Silent Speech Interface and its Challenges: A Survey

EEG-Based Silent Speech Interface and its Challenges: A Survey

Fuzzy Logic Based Eye-Brain Controlled Web Access System

Common Spatial Pattern in Frequency Domain for Feature Extraction and Classification of Multichannel EEG Signals

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