NDLI: Detecting Behavioral Microsleeps using EEG and LSTM Recurrent Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Davidson, P.R. Jones, R.D. Peiris, M.T.R.
Copyright Year	2005
Description	Author affiliation: Van der Veer Inst. for Parkinson's & Brain Res., Christchurch (Davidson, P.R.; Jones, R.D.; Peiris, M.T.R.)
Abstract	Lapses in visuomotor performance are often associated with behavioral microsleep events. Experiencing a lapse of this type while performing an important task can have catastrophic consequences. A warning system capable of reliably detecting patterns in EEG occurring before or during a lapse has the potential to save many lives. We are developing a behavioral microsleep detection system which employs long short-term memory (LSTM) recurrent neural networks. To train and validate the system, EEG, facial video and tracking data were collected from 15 subjects performing a visuomotor tracking task for 2 1-hour sessions. This provided behavioral information on lapse events with good temporal resolution. We developed an automated behavior rating system and trained it to estimate the mean opinion of 3 human raters on the likelihood of a lapse. We then trained an LSTM neural network to estimate the output of the lapse rating system given only EEG spectral data. The detection system was designed to operate in real-time without calibration for individual subjects. Preliminary results show the system is not reliable enough for general use, but results from some tracking sessions encourage further investigation of the reported approach
Starting Page	5754
Ending Page	5757
File Size	318573
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Recurrent neural networks Target tracking Humans Biomedical engineering Facial features Eyes Electrooculography Multilayer perceptrons Neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

EEG-Based Lapse Detection With High Temporal Resolution

Detecting Behavioral Microsleeps from EEG Power Spectra

Fractal Dimension of the EEG for Detection of Behavioural Microsleeps

The relationship between behavioural microsleeps, visuomotor performance and EEG theta

Facial feature location using multilayer perceptrons and micro-features

EEG and artifact classification using a neural network

Using time-dependent neural networks for EEG classification

Prediction of hand movement from the event-related EEG using neural networks

Facial expression recognition using graph-based features and artificial neural networks

Detecting Behavioral Microsleeps using EEG and LSTM Recurrent Neural Networks

Similar Documents

EEG-Based Lapse Detection With High Temporal Resolution

Detecting Behavioral Microsleeps from EEG Power Spectra

Fractal Dimension of the EEG for Detection of Behavioural Microsleeps

The relationship between behavioural microsleeps, visuomotor performance and EEG theta

Facial feature location using multilayer perceptrons and micro-features

EEG and artifact classification using a neural network

Using time-dependent neural networks for EEG classification

Prediction of hand movement from the event-related EEG using neural networks

Facial expression recognition using graph-based features and artificial neural networks

Detecting Behavioral Microsleeps using EEG and LSTM Recurrent Neural Networks