Detecting boredom from eye gaze and EEG

Kim, Joochan; Seo, Jungryul; Laine, Teemu H.

doi:10.1016/j.bspc.2018.05.034

Cited by 34 publications

(26 citation statements)

References 27 publications

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“…An evolutionary computation was then proposed as a competitive algorithm in emotion recognition and EMOTIV INSIGHT was used for performance evaluation [33]. Furthermore, MUSE and certain other physiological datasets were used in another work involving boredom detection during video clip viewing [34]. Data collected from the MUSE headband were also shown to be able to predict the affective states and their magnitudes [35].…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

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Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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“…Sources are supposed mutually uncorrelated and stationary. In some studies it is reported that SOBI is capable of functionally separating sources which are physiologically interpretable [24][25][26][27]. SOBI is really robust in low SNRs [27][28][29][30][31][32].…”

Section: Blind Source Separation and Sobimentioning

confidence: 99%

EEG Source Identification through Phase Space Reconstruction and Complex Networks

Soroush¹

2020

Preprint

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Artifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind source separation (BSS) methods have been receiving a great deal of attention in recent decades since they are considered routine and standard signal processing tools and are commonly used to eliminate artifacts and noise. Most studies, mainly EEG-related ones, apply BSS methods in preprocessing sections to achieve better results. On the other hand, BSS methods should be followed by a classifier in order to identify artifactual sources and remove them in next steps. Therefore, artifact identification is always a challenging problem while employing BSS methods. Additionally, removing all detected artifactual components leads to loss of information since some desire information related to neural activity leaks to these sources. So, an approach should be employed to suppress the artifacts and reserve neural activity. In this study, a new hybrid method is proposed to automatically separate and identify electroencephalogram (EEG) sources with the aim of classifying and removing artifacts. Automated source identification is still a challenge. Researchers have always made efforts to propose precise, fast and automated source verification methods. Reliable source identification has always been of great importance. This paper addresses blind source separation based on second order blind identification (SOBI) as it is reportedly one of the most effective methods in EEG source separation problems. Then a new method for source verification is introduced which takes advantage of components phase spaces and their dynamics. A new state space called angle space (AS) is introduced and features are extracted based on the angle plot (AP) and Poincare planes. Identified artifactual sources are eliminated using stationary wavelet transform (SWT). Simulated, semi-simulated and real EEG signals are employed to evaluate the proposed method. Different simulations are performed and performance indices are reported. Results show that the proposed method outperforms most recent studies in this subject.

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“…Gamma brain wave (above 30 Hz) is the fastest band, which is of high frequency, associated with the cognitive, attention tasks, and processes information from different brain areas. Beta brain wave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) is the next fast band in line and is associated with consciousness, active thinking, attention, focus, and anxiousness. Beta is high when individuals engage in problem-solving and critical thinking.…”

Section: Introductionmentioning

confidence: 99%

“…Theta brain wave (4)(5)(6)(7)(8) can be detected in deep relaxation, sleep, and meditation. Delta wave (0-4 Hz) is the slowest and low-frequency band associated with deep sleep and dreamless sleep [21,22].…”

Section: Introductionmentioning

confidence: 99%

Effects of Daily Probiotics Supplementation on Anxiety Induced Physiological Parameters among Competitive Football Players

2020

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Competitive football players who undergo strenuous training and frequent competitions are more vulnerable to psychological disorders. Probiotics are capable of reducing these psychological disorders. The present study aimed to determine the effect of daily probiotics supplementation on anxiety induced physiological parameters among competitive football players. The randomized, double-blinded, placebo-controlled trial was conducted on 20 male footballers who received either probiotics (Lactobacillus Casei Shirota strain 3 × 1010 colony forming units (CFU) or a placebo drink over eight weeks. Portable biofeedback devices were used to measure the electroencephalography, heart rate, and electrodermal responses along with cognitive tests at the baseline, week 4, and week 8. Data were statistically analyzed using mixed factorial ANOVA and results revealed that there is no significant difference between the probiotic and placebo groups for heart rate (61.90 bpm ± 5.84 vs. 67.67 bpm ± 8.42, p = 0.09) and electrodermal responses (0.27 µS ± 0.19 vs. 0.41 µS ± 0.12, p = 0.07) after eight weeks. Similarly, brain waves showed no significant changes during the study period except for the theta wave and delta wave at week 4 (p < 0.05). The cognitive test reaction time (digit vigilance test) showed significant improvement in the probiotic group compared to the placebo (p < 0.05). In conclusion, these findings suggest that daily probiotics supplementation may have the potential to modulate the brain waves namely, theta (relaxation) and delta (attention) for better training, brain function, and psychological improvement to exercise. Further research is needed to elucidate the mechanism of current findings.

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Detecting boredom from eye gaze and EEG

Cited by 34 publications

References 27 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

EEG Source Identification through Phase Space Reconstruction and Complex Networks

Effects of Daily Probiotics Supplementation on Anxiety Induced Physiological Parameters among Competitive Football Players

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