Brain Computer Interfaces

Brandman, David M.; Hochberg, Leigh R.

doi:10.1002/9781118816028.ch9

Cited by 3 publications

(3 citation statements)

References 140 publications

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“…The BCI sensor choice determines the temporal and spatial resolution of the neural signal [2], [8], [24]. The temporal resolution refers to the timescale over which neural signals are sampled and can be detected.…”

Section: Intracortical Recording Devices As Bci Sensorsmentioning

confidence: 99%

Review: Human Intracortical Recording and Neural Decoding for Brain–Computer Interfaces

Brandman

Cash

Hochberg

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain Computer Interfaces (BCIs) use neural information recorded from the brain for voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.

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Section: Intracortical Recording Devices As Bci Sensorsmentioning

confidence: 99%

Review: Human Intracortical Recording and Neural Decoding for Brain–Computer Interfaces

Brandman

Cash

Hochberg

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Brain‐computer interface allows individuals to manage a device by modifying neurological activity in their brains without using any other nervous system activation [1]. As a result, BCIs have been offered as possible adaptive equipment for individuals who have trouble asserting control over their various neurological systems.…”

Section: Introductionmentioning

confidence: 99%

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

A¹,

S²,

Mahendran

et al. 2022

Asian Journal of Control

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Brain–Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor mental activity with the ultimate objective of allowing people to communicate with computers only via their thoughts. Users must create precise cerebral activity patterns that the system uses as control signals to do this. A common activity used to elicit such signals is Motor Imagery (MI), in which certain signals are created in the sensorimotor cortex while imagining the movements. The three phases of the traditional EEG–BCI processing pipeline are preprocessing, feature extraction, and classification. We provide categorization advances and track performance gains in 4‐class MI‐based BCIs. In this study, 4‐class MI events are produced via an illusory elevation of the left hand, right hand, feet, and tongue. Finally, a two‐phase classification technique is provided with ANN classifiers being used in the first phase to discriminate between different pair‐wise MI tasks. Secondly, an adaptive SVM classifier is used to assess the user's end task based on the weighted outputs of the classifiers. An adaptive classifier is one technique to maintain consistency in performance, reduce training time, and eliminate non‐stationaries, all of which are required for efficient BCI performance. The suggested approach outperformed conventional two‐stage classification algorithms on MI data, according to experimental findings. The average classification accuracy of this technique is 96% for datasets BCI competition IV 2a. This is a 4% improvement over the comparison approach.

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“…While aspects of this review will also be relevant to recording-only implantable devices, there are a number of excellent reviews covering the design, preclinical studies and clinical application of recording electrodes (e.g. [14][15][16][17]) and are therefore not specifically considered here.…”

Section: Introductionmentioning

confidence: 99%

The development of neural stimulators: a review of preclinical safety and efficacy studies

et al. 2018

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The pipeline from concept to commercialisation of these devices is long and expensive; careful attention to both device design and its preclinical evaluation will have significant impact on the duration and cost associated with taking a device through to commercialisation. Carefully controlled in vitro and in vivo studies together with ex vivo and human cadaver trials are key components of a thorough preclinical evaluation of any new neural stimulator.

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Brain Computer Interfaces

Cited by 3 publications

References 140 publications

Review: Human Intracortical Recording and Neural Decoding for Brain–Computer Interfaces

Review: Human Intracortical Recording and Neural Decoding for Brain–Computer Interfaces

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

The development of neural stimulators: a review of preclinical safety and efficacy studies

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