Brain computer interface: A comprehensive survey

Tiwari, Neha; Edla, Damodar Reddy; Dodia, Shubham; Bablani, Annushree

doi:10.1016/j.bica.2018.10.005

Cited by 39 publications

(27 citation statements)

References 45 publications

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“…Researchers have already faced some severe issues while exploring BCI paradigms, including training time and fatigue, signal processing, and novel decoders; shared control to supervisory control in closed-loop; etc. Tiwari, N. et al [7] provided a complete assessment of the evolution of BCI and a fundamental introduction to brain functioning. An extensive comprehensive revision of the anatomy of the human brain, BCI, and its phases; the methods for extracting signals; and the algorithms for putting the extracted information to use was offered.…”

Section: Refmentioning

confidence: 99%

Brain-Computer Interface: Advancement and Challenges

Mridha

Das

Kabir

et al. 2021

Sensors

103

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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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Section: Refmentioning

confidence: 99%

Brain-Computer Interface: Advancement and Challenges

Mridha

Das

Kabir

et al. 2021

Sensors

103

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“…A new cluster separability analysis method proposed by Tiwari et al [14]suggests that a clusterbased classification method could work on EEG data if the clusters are separable enough. The cluster analysis uses the intra and inter-cluster distances to calculate a discrimination value that represents the separability of two clusters.…”

Section: Classificationmentioning

confidence: 99%

A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

Abdulwahab

Khleaf²,

Jasim³

2021

ETJ

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A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different methods for adapting the signals from the brain. It has been mentioned that various kinds of surveys in the past to serve the purpose of the present research. This paper shows a simple and easy analysis of each technique and its respective benefits and drawbacks, including signal acquisition, signal pre-processing, feature classification and classification. Finally, discussed is the application of EEG-based BCI.

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“…Further progress has been reported in [23] where variational autoencoder (VAE) was used to model and predict single-trial firing patterns from large population of neurons. In addition to the aforementioned notable works, [24]- [26] provide comprehensive reviews of common classification methods in BCIs including DNNs.…”

Section: B Related Workmentioning

confidence: 99%

Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data

Angjelichinoski

Soltani

Choi

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for decoding motor intentions. Yet, in many instances, brain-computer interfaces (BCIs) rely on simple classification methods, circumventing deep neural networks (DNNs) due to limited training data. This paper leverages the robustness of several important results in non-parametric regression to harness the potentials of deep learning in limited data setups. We consider a solution that combines Pinsker's theorem as well as its adaptively optimal counterpart due to James-Stein for feature extraction from LFPs, followed by a DNN for classifying motor intentions. We apply our approach to the problem of decoding eye movement intentions from LFPs collected in macaque cortex while the animals perform memoryguided visual saccades to one of eight target locations. The results demonstrate that a DNN classifier trained over the Pinsker features outperforms the benchmark method based on linear discriminant analysis (LDA) trained over the same features.

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Brain computer interface: A comprehensive survey

Cited by 39 publications

References 45 publications

Brain-Computer Interface: Advancement and Challenges

Brain-Computer Interface: Advancement and Challenges

A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface

Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data

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