Adaptation of Common Spatial Patterns based on mental fatigue for motor-imagery BCI

Talukdar, Upasana; Hazarika, Shyamanta M.; Gan, John Q.

doi:10.1016/j.bspc.2019.101829

Cited by 22 publications

(9 citation statements)

References 29 publications

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“…It can effectively project high‐dimensional data towards one direction and then perform linear classification 41 . In binary Fisher's LDA, maximum separation is searched between two classes for optimum projection 42 . In present study, a new four‐class Fisher's Linear Discriminant Analysis (LDA) criterion is proposed as an objective function and referred as multi‐class Fisher's LDA (MDA).…”

Section: Methodsmentioning

confidence: 99%

“…41 In binary Fisher's LDA, maximum separation is searched between two classes for optimum projection. 42 In present study, a new fourclass Fisher's Linear Discriminant Analysis (LDA) criterion is proposed as an objective function and referred as multi-class Fisher's LDA (MDA). The developed MDA acts as the objective function in SOGWO based optimization to remove irrelevant and redundant channels from the available dataset.…”

Section: Selective Opposition Gwomentioning

confidence: 99%

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A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Jindal

Upadhyay

Singh

2022

Int J Imaging Syst Tech

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Multi-class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG channels lead to redundant information extraction and would reduce the distinction among various MI tasks. Therefore, optimal channel selection from multi-channel EEG activity still remains a challenging task. In this study, to enhance the multi-class BCI system's performance, a novel channel selection, and features optimization methodology have been proposed.First, multi-channel EEG dataset is reduced to an optimum no. of channels subset using developed MDA-SOGWO based EEG channel selection criterion.After that, the discriminable feature-set is generated from time, frequency, and FAWT based time-frequency domain of EEG dataset. Then, an informative feature-set is constructed from extracted features through CCA-RFE based feature selection criterion. Finally, training and validation of selected feature-set are carried out using ELM, LDA, RF, and MLP classifiers. The proposed methodology is evaluated on multi-class MI datasets of BCI Competition IV 2a and BCI Competition III 3a, yielding classification accuracy of 85.65% and 95.15%, respectively. The classification results pose the proposed methodology as an efficient method for future BCI system design.

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Section: Methodsmentioning

confidence: 99%

Section: Selective Opposition Gwomentioning

confidence: 99%

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Jindal

Upadhyay

Singh

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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“…It is believed that the sEEG, despite not being the newest measurement technology, holds great potential for the BCI applications as it offers the measurement of brain structures, which cannot be reached with the ECoG, also, the sEEG enables to decode the memory-related processes and limbic activity, which can also be used in order to either supplement or further enhance decoding of other cognitive processes. The sEEG can be a future of invasive-BCIs [182,187].…”

Section: The Newest Trends and Further Development Paths In Bcismentioning

confidence: 99%

Summary of over Fifty Years with Brain-Computer Interfaces—A Review

et al. 2021

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Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

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“…Some researches [61], [121] attempted to reduce subjects fatigue by employing visual stimuli in higher frequencies, however, they cannot be adaptive according to the state of mental fatigue. Recently, Talukdar et al [122] proposed an adaptive structure for the CSP based on the mental fatigue of the subjects for motor-imagery BCI, which can adapt the CSP through employing LDA, providing a potential solution for SSVEP-based BCI systems.…”

Section: Mental Fatiguementioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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Adaptation of Common Spatial Patterns based on mental fatigue for motor-imagery BCI

Cited by 22 publications

References 29 publications

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design

Summary of over Fifty Years with Brain-Computer Interfaces—A Review

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

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