Semiasynchronous BCI Using Wearable Two-Channel EEG

Jiang, Yubing; Hau, Nguyen Trung; Chung, Wan-Young

doi:10.1109/tcds.2017.2716973

Cited by 16 publications

(14 citation statements)

References 19 publications

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“…The strong correlation between EEG signals and mental tasks has led to many user centric applications such as virtual spellers for the communication [7], functional electrical stimulation (FES) based neuro-prosthesis for tetraplegics [4], hand exoskeleton control [8], [9], [10], [11] and telepresence for personal assistance [12]. In spite of the seemingly bright prospect of the BCI technology, there are some practical challenges regarding the robustness, accuracy, and information transfer rate (ITR) of such systems [13], [14], [15]. The non-stationary nature of neurophysiological signals and dynamics of brain activity make the EEG-based BCI, a dynamically varying system, and thus improving its learning performance is a challenging task [16].…”

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confidence: 99%

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

Chowdhury

Raza

Meena

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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Abstract-A major issue in electroencephalogram (EEG) based brain-computer interfaces (BCIs) is the intrinsic nonstationarities in the brain waves, which may degrade the performance of the classifier, while transitioning from calibration to feedback generation phase. The non-stationary nature of the EEG data may cause its input probability distribution to vary over time, which often appear as a covariate shift. To adapt to the covariate shift, we had proposed an adaptive learning method in our previous work and tested it on offline standard datasets. This paper presents an online BCI system using previously developed covariate shift detection (CSD)-based adaptive classifier to discriminate between mental tasks and generate neurofeedback in the form of visual and exoskeleton motion. The CSD test helps prevent unnecessary retraining of the classifier. The feasibility of the developed online-BCI system was first tested on 10 healthy individuals, and then on 10 stroke patients having hand disability. A comparison of the proposed online CSD-based adaptive classifier with conventional non-adaptive classifier has shown a significantly (p ≤ 0.01) higher classification accuracy in both the cases of healthy and patient groups. The results demonstrate that the online CSDbased adaptive BCI system is superior to the non-adaptive BCI system and it is feasible to be used for actuating hand exoskeleton for the stroke-rehabilitation applications.

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confidence: 99%

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

Chowdhury

Raza

Meena

et al. 2018

IEEE Trans. Cogn. Dev. Syst.

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“…Moreover, feedback can help in modulating the MI ability of users. For instance, Jiang et al [82] deal with the creation of a BCI system that uses discrete and continuous feedback in order to improve practicability and training efficiency. The results show that continuous feedback successfully improves imagery ability and decreases the control time.…”

Section: Bci Application and Feedbackmentioning

confidence: 99%

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

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In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

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“…However, Cheng proposed a convolutional neural network, followed by a fully connected network (CNN-FC), while Antelis proposed Dendrite morphological neural networks (DMNN). Another approach is to let the subject achieve a set number of consistent right/left classification within a set period for an action to be taken, thus confirming the command and avoiding randomness [ 164 ], both adding a classifier and classifying multiple times, adds computational time and complexity to the system, with the latter also adding the time required for classification. Sun et al [ 165 ] suggested a method that avoids these constraints by using a threshold on an existing classifier that separates idle from MI task-related.…”

Section: Key Issues In MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

107

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Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

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Semiasynchronous BCI Using Wearable Two-Channel EEG

Cited by 16 publications

References 19 publications

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

Online Covariate Shift Detection-Based Adaptive Brain–Computer Interface to Trigger Hand Exoskeleton Feedback for Neuro-Rehabilitation

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

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