Toward a Hybrid Passive BCI for the Modulation of Sustained Attention Using EEG and fNIRS

Karran, Alexander J.; Demazure, Théophile; Léger, Pierre‐Majorique; Labonté-LeMoyne, Élise; Sénécal, Sylvain; Frédette, Marc; Babin, Gilbert

doi:10.3389/fnhum.2019.00393

Cited by 19 publications

(8 citation statements)

References 66 publications

(82 reference statements)

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“…It meant that different methods explored distinct information and became complementary to each other thereby improving classification performance. The results agreed with the conclusion in the literature [ 29 , 38 , 39 , 42 ]. The present paper is an advance on previous studies because it generated new knowledge about regional information by comparing the foci of independent types of features.…”

Section: Discussionsupporting

confidence: 92%

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EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning

Cao

Garro

Zhao

2022

Sensors

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There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG–functional near-infrared spectroscopy (EEG–fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5–4 Hz), theta (4–7 Hz) and alpha (8–15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8).

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Section: Discussionsupporting

confidence: 92%

“…Chu et al [ 39 ] stated that the alpha-power of O1 indicated differences between multi-level workloads. Regarding fNIRS, the prefrontal areas were well-accepted for measuring variations in mental workload [ 40 , 41 , 42 ]. However, there was limited research pointing out a determined channel that contributes the most.…”

Section: Discussionmentioning

confidence: 99%

EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning

Cao

Garro

Zhao

2022

Sensors

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“…The analysis of the α, β, and θ waves revealed a similar effect in the EEG index when driving in the middle and the rear end of the tunnel. Researchers have recently studied the sustained attention and the attention behavior in distracted driving (Wang et al, 2013 ; Pallavi and Harish, 2016 ; Ding et al, 2019 ; Karran et al, 2019 ), especially the reduction and oscillations of the α - wave energy and the increase in the θ - wave energy (Klimesch et al, 1998 ; Wang et al, 2015 ). It could be speculated that the trends of these wave energies were related to the attention–concentration behavior of drivers during tunnel driving.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

et al. 2021

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In recent years, the mileage of the tunnels has substantially increased with the rapid highway construction that led to increasing highway tunnels. Most studies on tunnel accidents have mainly focused on the external environments, such as tunnel structure, traffic volume, and lighting. In addition, although many studies on mental load of drivers have been conducted for public roads, such studies for highway tunnels have been limited. In this study, three scenarios with different front vehicle speeds (60, 45, and 30 km/h) in a two-lane long tunnel (one lane in each travel direction) were evaluated using a driving simulator. The experiment involved 24 participants (14 men and 10 women) with an average age of 25.8 years and an average experience of 3.2 years. The electroencephalogram (EEG) technology was used to collect the leading EEG indicators during the driving simulation of the scenarios: α, β, and θ waves and the wave ratio, (α + θ)/β. According to the β-wave energy measurements, the alertness of drivers was the lowest at 45 km/h after adapting to the tunnel environment, indicating that the drivers were more comfortable at this speed. This preliminary finding should help in determining the speed limit in this type of tunnel.

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“…[1,2]. The main purpose of a BCI is to equip physically impaired people, especially with motor disabilities, with the facility to communicate with the help of their brain signals [3,4]. A BCI is used to detect and interpret brain signals to control the devices [5].…”

Section: Introductionmentioning

confidence: 99%

Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

Arif¹,

Khan²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

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For BCI systems, it is important to have an accurate and less complex architecture to control a device with enhanced accuracy. In this paper, a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface (BCI). An integrated classifier has been developed for achieving better classification accuracy using two modalities. An integrated EEG-fNIRS-based vector-phase analysis (VPA) has been conducted. An open-source dataset collected at the Technische Universität Berlin, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals of 26 healthy participants during n-back tests, has been used for this research. Instrumental and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both modalities. With resting state threshold circle, VPA has been used to detect a hemodynamic response in fNIRS signals, whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each trial. Multiple threshold circles are drawn in the vector plane, where each circle is drawn after task completion in each trial of EEG signal. Finally, both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for activity. Results of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy, that is 91.35%, as compared to other classifiers such as support vector machine (SVM), convolutional neural networks (CNN), deep neural networks (DNN) and VPA (with dual-threshold circles) with classification accuracies 82%, 89%, 87% and 86% respectively. Outcomes of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.

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Toward a Hybrid Passive BCI for the Modulation of Sustained Attention Using EEG and fNIRS

Cited by 19 publications

References 66 publications

EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning

EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning

Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph

Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

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