A Review on Brain Imaging Techniques for BCI Applications

Bhattacharyya, Saugat; Khasnobish, Anwesha; Ghosh, Poulami; Mazumder, Ankita; Tibarewala, D. N.

doi:10.4018/978-1-4666-8811-7.ch003

Cited by 4 publications

(4 citation statements)

References 81 publications

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“…However, given the importance of this process, we have separated its description into a separate section of the paper. Feature extraction is the process of distinguishing and identifying the pertinent signal characteristics from raw neural data [18] , [19] . Examples include event-related potential (ERP) components such as N2pc, N400, P300 and the error related negativity (ERN), event-related (de) synchronization (ERD/S) steady state visually evoked potentials (SSVEP), Somatosensory Evoked Potential (SSEP), Local Field Potential (LFP) [20] and sensorimotor rhythm (SMR).…”

Section: Functional Modelmentioning

confidence: 99%

A Functional Model for Unifying Brain Computer Interface Terminology

Easttom

Bianchi

Valeriani

et al. 2021

IEEE Open J. Eng. Med. Biol.

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Brain Computer Interface (BCI) technology is a critical area both for researchers and clinical practitioners. The IEEE P2731 working group is developing a comprehensive BCI lexicography and a functional model of BCI. The glossary and the functional model are inextricably intertwined. The functional model guides the development of the glossary. Terminology is developed from the basis of a BCI functional model. This paper provides the current status of the P2731 working group's progress towards developing a BCI terminology standard and functional model for the IEEE.

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Section: Functional Modelmentioning

confidence: 99%

A Functional Model for Unifying Brain Computer Interface Terminology

Easttom

Bianchi

Valeriani

et al. 2021

IEEE Open J. Eng. Med. Biol.

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“…Different types of neuroimaging techniques can be used to implement BCIs, i.e., electroencephalography (EEG), magnetoencephalography (MEG), functional Magnetic Resonance Imaging (fMRI), functional Near-Infrared Spectroscopy (fNIRS), among others ( Zou et al, 2019 ). The most common modality is the EEG, since it provides a portable, inexpensive, non-invasive solution to measure brain activity with high temporal resolution ( Sitaram et al, 2007 ; Bhattacharyya et al, 2017 ; Deshpande et al, 2017 ; Zou et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

et al. 2020

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There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.

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“…BCIs are a potential method to promote the independence of disabled persons because of the BCI's ability to bypass non-functional neural pathways [8]. In particular, to support patients' mobility and accessibility, a diverse set of BCI applications has been developed, such as BCI-controlled wheelchairs, orthoses, prostheses, and exoskeletons [37][38][39][40][41][42][43][44][45] using various brain imaging technologies (e.g., electroencephalography (EEG), magnetoencephalographic (MEG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET)) [46,47]. Among the various brain imaging methods, the EEG method has been most well-studied because of its advantages such as low prices, convenience, mobility, large cortical coverage, and high temporal resolution compared to other methods [48].…”

Section: Smr-based Bci-controlled Fes Systemsmentioning

confidence: 99%

Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation

et al. 2020

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Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented.

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A Review on Brain Imaging Techniques for BCI Applications

Cited by 4 publications

References 81 publications

A Functional Model for Unifying Brain Computer Interface Terminology

A Functional Model for Unifying Brain Computer Interface Terminology

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation

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