Hand motion identification of grasp-and-lift task from electroencephalography recordings using recurrent neural networks

An, Jinwon; Cho, Sungzoon

doi:10.1109/bigcomp.2016.7425963

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Therefore, 154 papers were selected for inclusion in the analysis. 7 The queries used for each database are available at http://dl-eeg.com 8 Since the Google Scholar search engine only allows searching full text or titles, and not titles and abstracts, the query was performed using the flag allintitle to search titles only. On arXiv and PubMed, however, both abstracts and titles were queried.…”

Section: Resultsmentioning

confidence: 99%

“…English journal and conference papers, as well as electronic preprints, published between January 2010 and July 2018, were chosen as the target of this review. PubMed, Google Scholar and arXiv were queried 7 to collect an initial list of papers containing specific search terms in their title or abstract 8 . Additional papers were identified by scanning the reference sections of these papers.…”

Section: Methodsmentioning

confidence: 99%

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Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…This is a GUI based work which moves the wheelchair without an ON-OFF button. The robotic movement can be implemented using Recurrent neural Networks as shown by the authors in [5]. Though the efficiency of such a system is very good, user training is mandatory which may not be desirable in some cases.…”

Section: Introductionmentioning

confidence: 99%

Home Automation Using Brain Signals

Mohan¹,

Mallikarjun²,

Beeresh³

et al. 2023

IJSRCSEIT

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In this time of digitization and computerization, the life of individuals is getting more straightforward as nearly everything is programmed. This interconnection of the things can be used to help people with physical disabilities including paralysis. The brain signals of such people can be harnessed to create a home automation system. In this work, the concentration levels of such individuals are extracted using the EEG signals. These signals are then used to control the electronic devices. The designed system has been tested to operate the switching of an electric bulb using brain signals. This system can also be extended to assist a physically impaired individual to have effective control over electrical and electronic appliances and devices within a home.

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