Cognitive Behavior Classification From Scalp EEG Signals

Dvořák, Dino; Shang, Andrea; Abdel-Baki, Samah G.; Suzuki, Wendy; Fenton, André A.

doi:10.1109/tnsre.2018.2797547

Cited by 38 publications

(18 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Canonical correlation and coherence have been used to infer brain network connectivity during periods of speech and rest [58]. Cognitive states have been decoded from EEG with synchrony operators [59]. Neither of those prior efforts achieved time resolution—both worked only with blocked analysis of many trials.…”

Section: Discussionmentioning

confidence: 99%

Decoding task engagement from distributed network electrophysiology in humans

et al. 2019

View full text Add to dashboard Cite

Objective.Here, our objective was to develop a binary decoder to detect task engagement in humans during two distinct, conflict-based behavioral tasks. Effortful, goal-directed decision-making requires the coordinated action of multiple cognitive processes, including attention, working memory and action selection. That type of mental effort is often dysfunctional in mental disorders, e.g. when a patient attempts to overcome a depression or anxiety-driven habit but feels unable. If the onset of engagement in this type of focused mental activity could be reliably detected, decisional function might be augmented, e.g. through neurostimulation. However, there are no known algorithms for detecting task engagement with rapid time resolution.Approach.We defined a new network measure, fixed canonical correlation (FCCA), specifically suited for neural decoding applications. We extracted FCCA features from local field potential recordings in human volunteers to give a temporally continuous estimate of mental effort, defined by engagement in experimental conflict tasks.Main results.Using a small number of features per participant, we accurately decoded and distinguished task engagement from other mental activities. Further, the decoder distinguished between engagement in two different conflict-based tasks within seconds of their onset.Significance.These results demonstrate that network-level brain activity can detect specific types of mental efforts. This could form the basis of a responsive intervention strategy for decision-making deficits.

show abstract

Section: Discussionmentioning

confidence: 99%

Decoding task engagement from distributed network electrophysiology in humans

et al. 2019

View full text Add to dashboard Cite

show abstract

“…BCI technology brings about a huge number of opportunities in the medical field especially for the treatment and rehabilitation for disabled people. Around the world, millions of people are affected by various forms of 15 disability and physical impairment caused by various reasons such as -born disability, old-age disability, early-age disability, accidental/unforeseen issues, serious health problems, and more [6]. For disabled people, it is extremely difficult or, in some cases, impossible to do day to day chores without the assistance of any caregiver.…”

Section: Introductionmentioning

confidence: 99%

“…Another way is to place electrodes on the surface of the scalp to measure activities from a huge group of neurons. This method is called noninvasive BCI [15,16]. In comparison with invasive BCI, it does not deliver better input signals as good as [17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

Miah

Rahman

Muhammod

et al. 2020

Preprint

View full text Add to dashboard Cite

The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal part of the biosignal classification in the brain-computer interface (BCI) applications. Currently, this bio-engineering based technology is being employed by researchers in various fields to develop cutting edge applications. The classification of real-time MI-EEG signal is the core computing and challenging task in these applications. It is well-known that the existing classification methods are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data. To improve the classification performance of real-time BCI applications, this paper presents a clustering-based ensemble technique and a developed brain game that distinguishes different human thoughts. At first, we have gathered the brain signals, extracted and selected informative features from these signals to generate training and testing sets. After that, we have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve Bayes, Decision Tree (DT), Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with suggested clustering-based ensemble technique. On average, the proposed ensemble technique improved the classification accuracy of roughly 5 to 15% compared to the existing methods. Finally, we have developed the targeted * Corresponding author Email address: dewanfarid@cse.uiu.ac.bd (Dewan Md. Farid) brain game employing our suggested ensemble technique. In this game, realtime EEG signal classification and prediction tabulation through animated ball are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/MI-EEG. Keywords: Brain Computer Interface (BCI); Brain Machine Interface (BMI); Brain Engineering; Motor Imagery Electroencephalogram (MI-EEG); Motor Cortex; Clustering; Ensemble Learning. 45 are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data [19, 20]. Sometimes, the signals are biased with artifacts and noise due to the low conductivity of the electrodes with the scalp [1]. The objective of this paper is to extend our prior works and ameliorates the classification performance by handling multiple electrodes/ neurons data at the same 50 time. The proposed clustering-based ensemble technique clustered the dataset 3 based on the position of the electrodes so that each cluster represents dissimilar information. It also selects the model dynamically based on the electrode locations to classify real-time EEG data. We have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naïve 55Bayes, Decision Tree, Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with the suggested technique. We also developed a brai...

show abstract

“…Electroencephalogram (EEG) is a noninvasive technology for measuring electrical brain activity with a high temporal resolution at the millisecond scale. Complementary to other neuroimaging technology (e.g., functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG)), EEG has emerged as a cost-efficient and portable tool that offers qualitative and quantitative assessments of the integrity of brain functions of interest with laboratory or clinical settings, e.g., motor function [3], cognitive behavior [4], affective responses [5], working memory [7], sleep staging [8], and driving fatigue [9]. Most practically, the user-/patient-friendly capability of the EEG modality facilitates a wide spectrum of brain-computer interface (BCI) applications in diverse domains, such as neurorehabilitation [11], robotic/device control [13], neuromarketing [15],…”

Section: Introductionmentioning

confidence: 99%

Cost-Efficient, Portable, and Custom Multi-Subject Electroencephalogram Recording System

Chuang

Lin

2019

IEEE Access

View full text Add to dashboard Cite

Multi-subject electroencephalogram (EEG) computing has attracted increasing interest in recent years. Commercial EEG headsets often lead to a space-required or cost-expensive setup for obtaining simultaneous recordings of multiple subjects. The sparsity or immobility of their electrode placements may compromise their realistic applicability. Thus, this work developed a cost-efficient, portable, and customizable system to mitigate these constraints. The developed system implemented two core hardware infrastructures, including an event broadcaster and a dry electrode-compatible EEG amplifier by assembling entirely low-cost, off-the-shelf electronic components. The broadcaster allowed distribution of event markers to multiple EEG amplifiers concurrently, whereas the amplifier transmitted the digitized, eventsynchronized EEG signals wirelessly. By conducting an oddball event-related potential (ERP) experiment with simultaneous recordings of three subjects on 10 days, the system reliably captured the time-and phaselocked ERP components (e.g., N100 and P300 amplitudes) by single-subject, multi-subject, and multi-day analytical approaches. The practicality and stability of the proposed system was empirically demonstrated in terms of the signal quality, EEG-event synchronization, and inter-amplifier coordination for a multi-subject setup. This work sheds light on how to economically facilitate and scale up multi-subject EEG computing for fundamental research and for brain-computer interface (BCI) applications in a larger subject population.

show abstract

Cognitive Behavior Classification From Scalp EEG Signals

Cited by 38 publications

References 43 publications

Decoding task engagement from distributed network electrophysiology in humans

Decoding task engagement from distributed network electrophysiology in humans

Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

Cost-Efficient, Portable, and Custom Multi-Subject Electroencephalogram Recording System

Contact Info

Product

Resources

About