NDLI: Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm

Please wait, while we are loading the content...

ACM Transactions on Embedded Computing Systems (TECS) : Volume 16

Issue 2, December 2016

Issue 1, October 2016

Guest Editorial: Special Issue on Virtual Prototyping of Parallel and Embedded Systems (ViPES)

VirtualSoC: A Research Tool for Modern MPSoCs

Simulating Reconfigurable Multiprocessor Systems-on-Chip with MPSoCSim

A Lightweight Framework for the Dynamic Creation and Configuration of Virtual Platforms in SystemC

A Scriptable Standard-Compliant Reporting and Logging Framework for SystemC

MPSoC Software Debugging on Virtual Platforms via Execution Control with Event Graphs

A Framework for Interconnection-Aware Domain-Specific Many-Accelerator Synthesis

Guest Editorial: Special Issue on Emerging Technologies in Embedded Software and Systems

Reducing Power Consumption and Latency in Mobile Devices Using an Event Stream Model

Image-Content-Aware I/O Optimization for Mobile Virtualization

Cache-Partitioned Preemption Threshold Scheduling

Adaptive Workload Management in Mixed-Criticality Systems

Automatic Parallelization of Multirate Block Diagrams of Control Systems on Multicore Platforms

Crosstalk-Aware Automated Mapping for Optical Networks-on-Chip

GPUrpc: Exploring Transparent Access to Remote GPUs

Game-Theory-Based Active Defense for Intrusion Detection in Cyber-Physical Embedded Systems

ScorePlus: A Software-Hardware Hybrid and Federated Experiment Environment for Smart Grid

Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm

Space-Efficient Index Scheme for PCM-Based Multiversion Databases in Cyber-Physical Systems

Modeling Distributed Real-Time Systems in TIOA and UPPAAL

Analysis and Scheduling of a Battery-Less Mixed-Criticality System with Energy Uncertainty

SPMPool: Runtime SPM Management for Memory-Intensive Applications in Embedded Many-Cores

Frequency-Aware ESL Power Estimation for ARM Cortex-A9 Using a Black Box Processor Model

Parallel SystemC Simulation for ESL Design

ACM Transactions on Embedded Computing Systems (TECS) : Volume 15

ACM Transactions on Embedded Computing Systems (TECS) : Volume 14

ACM Transactions on Embedded Computing Systems (TECS) : Volume 13

ACM Transactions on Embedded Computing Systems (TECS) : Volume 12

ACM Transactions on Embedded Computing Systems (TECS) : Volume 11

ACM Transactions on Embedded Computing Systems (TECS) : Volume 11S

ACM Transactions on Embedded Computing Systems (TECS) : Volume 10

ACM Transactions on Embedded Computing Systems (TECS) : Volume 9

ACM Transactions on Embedded Computing Systems (TECS) : Volume 8

ACM Transactions on Embedded Computing Systems (TECS) : Volume 7

ACM Transactions on Embedded Computing Systems (TECS) : Volume 6

ACM Transactions on Embedded Computing Systems (TECS) : Volume 5

ACM Transactions on Embedded Computing Systems (TECS) : Volume 4

ACM Transactions on Embedded Computing Systems (TECS) : Volume 3

ACM Transactions on Embedded Computing Systems (TECS) : Volume 2

ACM Transactions on Embedded Computing Systems (TECS) : Volume 1

Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm

Content Provider	ACM Digital Library
Author	Morshed, Bashir I. Majmudar, Charvi A.
Copyright Year	2016
Description	Author Affiliation: University of Memphis, Memphis, TN(Morshed, Bashir I.; Majmudar, Charvi A.)
Abstract	Electroencephalography (EEG) is a non-invasive technique to record brain activities in natural settings. Ocular Artifacts (OA) usually contaminates EEG signals, removal of which is critical for accurate feature extraction and classification. With the increasing adoption of wearable technologies, single-channel real-time EEG systems that often require real-time signal processing for immediate real-time feedback are becoming more prevalent. However, traditional OA removal algorithms usually require multiple channels of EEG data, are computationally expensive, and do not perform well in real-time. In this article, a new hybrid algorithm is proposed that autonomously detects OA and subsequently removes OA from a single-channel steaming EEG data in real-time. The proposed single EEG channel algorithm also does not require additional reference electrooculography (EOG) channel. The algorithm has also been implemented on an embedded hardware platform of single channel wearable EEG system (NeuroMonitor). The algorithm first detects the OA zones using an Algebraic approach and then removes these artifacts from the detected OA zones using the Discrete Wavelet Transform (DWT) decomposition method. The de-noising technique is applied only to the OA zone, which minimizes loss of neural information outside the OA zone. A qualitative and quantitative performance evaluation was carried out with a 0.5s epoch in overlapping sliding window technique using time-frequency analysis, mean square coherence, and correlation coefficient statistics. The hybrid OA removal algorithm demonstrated real-time operation with 3s latency on the PSoC-3-microcontroller-based EEG system. Successful implementation of OA removal from single-channel real-time EEG data using the proposed algorithm shows promise for real-time feedback applications of wearable EEG devices.
Starting Page	1
Ending Page	16
Page Count	16
File Format	PDF
ISSN	15399087
e-ISSN	15583465
DOI	10.1145/2983629
Volume Number	16
Issue Number	1
Journal	ACM Transactions on Embedded Computing Systems (TECS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-13
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artifact removal EEG signal processing Real-time algorithm Wearable
Content Type	Text
Resource Type	Article
Subject	Hardware and Architecture Software

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 Library 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

Real-time hybrid ocular artifact detection and removal for single channel EEG

Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data

Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform

Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings (2010)

Real-time estimation and 3d visualization of source dynamics and connectivity using wearable eeg.

18 EEG Artifact Removal Using A Wavelet Neural Network EEG Artifact Removal Using A Wavelet Neural Network

5.18 EEG Artifact Removal Using A Wavelet Neural Network EEG Artifact Removal Using A Wavelet Neural Network

Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm

Similar Documents

Real-time hybrid ocular artifact detection and removal for single channel EEG

Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data

Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform

Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems

An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings (2010)

Real-time estimation and 3d visualization of source dynamics and connectivity using wearable eeg.

18 EEG Artifact Removal Using A Wavelet Neural Network EEG Artifact Removal Using A Wavelet Neural Network

5.18 EEG Artifact Removal Using A Wavelet Neural Network EEG Artifact Removal Using A Wavelet Neural Network

Autonomous OA Removal in Real-Time from Single Channel EEG Data on a Wearable Device Using a Hybrid Algebraic-Wavelet Algorithm