Comparison of a Pragmatic and Regression Approach for Wearable EEG Signal Quality Assessment

Witteveen, Jolanda; Pradhapan, Paruthi; Mihajlović, Vojkan

doi:10.1109/jbhi.2019.2920381

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…It uses statistical features and is captured within an EEG epoch. More details on the estimator and its performance are reported here [45]. The signal quality indicator gives an output from 0 to 1, 0 corresponding to the highest quality and 1 to the lowest quality.…”

Section: Signal Quality Estimationmentioning

confidence: 99%

“…Spatial features are power spectral density in each frequency band (delta, theta, alpha, beta, and gamma) and differential entropy. The frequency band used included: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Those features were extracted for each epoch.…”

Section: Feature Extractionmentioning

confidence: 99%

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The Role of Features Types and Personalized Assessment in Detecting Affective State Using Dry Electrode EEG

Pradhapan

Velazquez

Witteveen

et al. 2020

Sensors

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Assessing the human affective state using electroencephalography (EEG) have shown good potential but failed to demonstrate reliable performance in real-life applications. Especially if one applies a setup that might impact affective processing and relies on generalized models of affect. Additionally, using subjective assessment of ones affect as ground truth has often been disputed. To shed the light on the former challenge we explored the use of a convenient EEG system with 20 participants to capture their reaction to affective movie clips in a naturalistic setting. Employing state-of-the-art machine learning approach demonstrated that the highest performance is reached when combining linear features, namely symmetry features and single-channel features, with nonlinear ones derived by a multiscale entropy approach. Nevertheless, the best performance, reflected in the highest F1-score achieved in a binary classification task for valence was 0.71 and for arousal 0.62. The performance was 10–20% better compared to using ratings provided by 13 independent raters. We argue that affective self-assessment might be underrated and it is crucial to account for personal differences in both perception and physiological response to affective cues.

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Section: Signal Quality Estimationmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

The Role of Features Types and Personalized Assessment in Detecting Affective State Using Dry Electrode EEG

Pradhapan

Velazquez

Witteveen

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Third, due to the numerous and miscellaneous music, it is difficult to simplify and improve the effect in a limited time. Witteveen, Pradhapan & Mihajlović (2019) found that β-level activity increased in high-pressure subjects. Al-Ezzi et al (2020) found that SW/FW (slow/fast wave) negatively correlates with anxiety.…”

Section: Related Workmentioning

confidence: 98%

The influence of music teaching appreciation on the mental health of college students based on multimedia data analysis

Shen

2023

PeerJ Computer Science

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The mental health problem of college students has gradually become the focus of people’s attention. The music appreciation course in university is a very effective approach of psychological counseling, and it is urgent to explore the role of music appreciation in psychological adjustment. Therefore, we propose an emotion classification model based on particle swarm optimization (PSO) to study the effect of inter active music appreciation teaching on the mental health of college students. We first extract musical features as input. Then, the extracted music appreciation features generate subtitles of music information. Finally, we weight the above features, input them into the network, modify the network through particle swarm optimization, and output the emotional class of music. The experimental results show that the music emotion classification model has a high classification accuracy of 82.6%, and can obtain the emotional categories included in interactive music appreciation, which is helpful to guide the mental health of college students in music appreciation teaching.

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“…The skewness of a signal is associated with how much the distribution of amplitudes deviates from a normal distribution and is thereby a measure of asymmetry. Skewness was used as a metric of quality in different types of signals such as pulse oximetry [46], ECG [47], and EEG [48]. Skewness is defined by…”

Section: Standard Deviation Of the Upper And Lower Envelopementioning

confidence: 99%

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

Idrobo-Ávila,

Bognár,

Krefting

et al. 2024

IEEE Open J. Eng. Med. Biol.

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Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysiswhich involves their joint analysis -can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. A correlation was found between entropy outliers and non-suitable electrocardiographic signals. Conclusions: The majority of data within the surgery dataset VitalDB demonstrated suitability for multimodal analysis.

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Comparison of a Pragmatic and Regression Approach for Wearable EEG Signal Quality Assessment

Cited by 11 publications

References 28 publications

The Role of Features Types and Personalized Assessment in Detecting Affective State Using Dry Electrode EEG

The Role of Features Types and Personalized Assessment in Detecting Affective State Using Dry Electrode EEG

The influence of music teaching appreciation on the mental health of college students based on multimedia data analysis

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis

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