Brain computer interface advancement in neurosciences: Applications and issues

Sharma, Suresh K; Chaturvedi, Jitender; Sharma, Anil Kumar

doi:10.1016/j.inat.2020.100694

Cited by 79 publications

(50 citation statements)

References 55 publications

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“…This overview encompasses a wide range of challenges and trends in BCI field. For specialized reviews on particular BCI topics we refer to the recent literature (McFarland et al, 2006 ; Schwartz et al, 2006 ; Bashashati et al, 2007 ; Lotte et al, 2007 , 2018 ; Matthews et al, 2007 ; Sitaram et al, 2007 ; Mak and Wolpaw, 2009 ; Menon et al, 2009 ; Nicolelis and Lebedev, 2009 ; Summerer et al, 2009 ; Vaadia and Birbaumer, 2009 ; Milan and Carmena, 2010 ; Min et al, 2010 ; Clausen, 2011 ; Krusienski et al, 2011 ; Liao et al, 2012 ; Nicolas-Alonso and Gomez-Gil, 2012 ; Shih et al, 2012 ; Jebari, 2013 ; McCullagh et al, 2014 ; Ahn and Jun, 2015 ; Jayaram et al, 2016 ; Lebedev and Nicolelis, 2017 ; Mudgal et al, 2020 ; Rashid et al, 2020 ; Saha and Baumert, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Progress in Brain Computer Interface: Challenges and Opportunities

Saha

Mamun

Ahmed

et al. 2021

Front. Syst. Neurosci.

196

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Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

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Section: Introductionmentioning

confidence: 99%

Progress in Brain Computer Interface: Challenges and Opportunities

Saha

Mamun

Ahmed

et al. 2021

Front. Syst. Neurosci.

196

View full text Add to dashboard Cite

show abstract

“…Invasive and partially invasive technologies remain limited to healthcare fields [50]. Medical grade brain-computer interfaces are often used in assisting people with damage to their cognitive or sensorimotor functions.…”

Section: Bci Systems Classificationmentioning

confidence: 99%

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

et al. 2021

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The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

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“…Electroencephalography (EEG) records the brain signals noninvasively with electrodes placed on the scalp [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Sessions Outcome for EEG Feature Extraction and Classification Methods in a Motor Imagery Task

Hrișcă-Eva¹,

Lazar²

2021

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The purpose of this research is to evaluate the performances of some features extraction methods and classification algorithms for the electroencephalographic (EEG) signals recorded in a motor task imagery paradigm. The sessions were performed by the same subject in eight consecutive years. Modeling the EEG signal as an autoregressive process (by means of Itakura distance and symmetric Itakura distance), amplitude modulation (using the amplitude modulation energy index) and phase synchronization (measuring phase locking value, phase lag index and weighted phase lag index) are the methods used for getting the appropriate information. The extracted features are classified using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance, support vector machine and k nearest neighbor classifiers. The highest classifications rates are achieved when Itakura distance with Mahalanobis distance based classifier are applied. The outcomes of this research may improve the design of assistive devices for restoration of movement and communication strength for physically disabled patients in order to rehabilitate their lost motor abilities and to improve the quality of their daily life.

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Brain computer interface advancement in neurosciences: Applications and issues

Cited by 79 publications

References 55 publications

Progress in Brain Computer Interface: Challenges and Opportunities

Progress in Brain Computer Interface: Challenges and Opportunities

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

Multi-Sessions Outcome for EEG Feature Extraction and Classification Methods in a Motor Imagery Task

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