A review of recent trends in EEG based Brain-Computer Interface

Lahane, Prashant; Jagtap, Jay; Inamdar, Aditya; Karne, Nihal; Dev, Ritwik

doi:10.1109/iccids.2019.8862054

Cited by 28 publications

(18 citation statements)

References 17 publications

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“…This can also be noted that the overall BCI recognition levels vary from 40% to 80% in the database of 5-23 subjects and patients. However, there are needs in development; in future analyses, feature extraction algorithm based on connectivity features, statistical features and power connectivity features,[ 62 63 ] deep learning algorithms, and interactive training and testing modules should be considered to enhance the recognition of tasks used in the WNS system. Finally, it is also useful to explore the useful properties of brain patterns based on the spatial and frequency domain, feature extraction algorithm, and classification techniques.…”

Section: Discussionmentioning

confidence: 99%

Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine

et al. 2020

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“…This method divides the EEG data into spatially fixed and temporally independent components. In the case of computing and noise demonstrable, the ICA shows more efficiency [256].…”

Section: Independent Component Analysis (Ica)mentioning

confidence: 99%

“…This approach decreases noise across all recorded channels, but this does not address channelspecific noise and may inject noise into an otherwise clean channel. It is a spatial filter that can be thought of as the subtraction of shared EEG activity, retaining only the idle action of each EEG particular electrode [256].…”

Section: Common Average Reference (Car)mentioning

confidence: 99%

Brain-Computer Interface: Advancement and Challenges

Mridha

Das

Kabir

et al. 2021

Sensors

122

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Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

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“…State of the art development in BCI technology is driven primarily by active and reactive medical applications [see Lahane et al (2019) for a detailed review]. In this domain, brain signals primarily derived from motor-imagery tasks are used to enable the control of a prosthesis (Hong and Khan 2017) such as a robotic arm for users with spinal cord injury (Nicolas-Alonso and Gomez-Gil 2012;Müller-Putz et al 2005 or as input to controllers for wheelchairs (Carlson and Millan 2013).…”

Section: Brain-computer Interfacesmentioning

confidence: 99%

Enhancing Sustained Attention

et al. 2021

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Arguably, automation is fast transforming many enterprise business processes, transforming operational jobs into monitoring tasks. Consequently, the ability to sustain attention during extended periods of monitoring is becoming a critical skill. This manuscript presents a Brain-Computer Interface (BCI) prototype which seeks to combat decrements in sustained attention during monitoring tasks within an enterprise system. A brain-computer interface is a system which uses physiological signals output by the user as an input. The goal is to better understand human responses while performing tasks involving decision and monitoring cycles, finding ways to improve performance and decrease on-task error. Decision readiness and the ability to synthesize complex and abundant information in a brief period during critical events has never been more important. Closed-loop control and motivational control theory were synthesized to provide the basis from which a framework for a prototype was developed to demonstrate the feasibility and value of a BCI in critical enterprise activities. In this pilot study, the BCI was implemented and evaluated through laboratory experimentation using an ecologically valid task. The results show that the technological artifact allowed users to regulate sustained attention positively while performing the task. Levels of sustained attention were shown to be higher in the conditions assisted by the BCI. Furthermore, this increased cognitive response seems to be related to increased on-task action and a small reduction in on-task errors. The research concludes with a discussion of the future research directions and their application in the enterprise.

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A review of recent trends in EEG based Brain-Computer Interface

Cited by 28 publications

References 17 publications

Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine

Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine

Brain-Computer Interface: Advancement and Challenges

Enhancing Sustained Attention

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