SparrKULee: A Speech-evoked Auditory Response Repository of the KU Leuven, containing EEG of 85 participants

Accou, Bernd; Bollens, Lies; Gillis, Marlies; Verheijen, Wendy; hamme, Hugo Van; Francart, Tom

doi:10.1101/2023.07.24.550310

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…SparrKULee Dataset SparrKULee dataset [42] is a speech-evoked EEG dataset from the KU Leuven University containing 64-channel EEG recordings from 85 participants, each of whom listened to 90-150 minutes of natural speech. We used this dataset because EEG recordings were longer than 1 hour to ensure a sufficient amount of data for each subject.…”

Section: Datasetmentioning

confidence: 99%

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Zhang

Wang

et al. 2021

Front. Neurosci.

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Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3–3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human–machine interaction system, such as rehabilitation robot.

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Section: Datasetmentioning

confidence: 99%

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Zhang

Wang

et al. 2021

Front. Neurosci.

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“…by assigning distinct labels to data segments originating from the first and second half of each trial, respectively). Since our dataset did not consist of trials that satisfy this condition, we performed this analysis on a separate, publicly-available dataset, namely SparrKULee (Accou et al 2023.…”

Section: Unsupervised Classifiers Can Leverage Within-trial Feature D...mentioning

confidence: 99%

What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention

Rotaru,

Geirnaert,

Heintz

et al. 2024

J. Neural Eng.

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Objective. Spatial auditory attention decoding (Sp-AAD) refers to the task of identifying the direction of the speaker to which a person is attending in a multi-talker setting, based on the listener's neural recordings, e.g., electroencephalography (EEG). The goal of this study is to thoroughly investigate potential biases when training such Sp-AAD decoders on EEG data, particularly eye-gaze biases and latent trial-dependent confounds, which may result in Sp-AAD models that decode eye-gaze or trial-specific fingerprints rather than spatial auditory attention. Approach. We designed a two-speaker audiovisual Sp-AAD protocol in which the spatial auditory and visual attention were enforced to be either congruent or incongruent, and we recorded EEG data from sixteen participants undergoing several trials recorded at distinct timepoints. We trained a simple linear model for Sp-AAD based on common spatial patterns (CSP) filters in combination with either linear discriminant analysis (LDA) or k-means clustering, and evaluated them both across- and within-trial. Main results. We found that even a simple linear Sp-AAD model is susceptible to overfitting to confounding signal patterns such as eye-gaze and trial fingerprints (e.g., due to feature shifts across trials), resulting in artificially high decoding accuracies. Furthermore, we found that changes in the EEG signal statistics across trials deteriorate the trial generalization of the classifier, even when the latter is retrained on the test trial with an unsupervised algorithm. Significance. Collectively, our findings confirm that there exist subtle biases and confounds that can strongly interfere with the decoding of spatial auditory attention from EEG. It is expected that more complicated non-linear models based on deep neural networks, which are often used for Sp-AAD, are even more vulnerable to such biases. Future work should perform experiments and model evaluations that avoid and/or control for such biases in Sp-AAD tasks.

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Transformer-Based Model for Auditory EEG Decoding

Chen,

Liu,

Feng

et al. 2024

Communications in Computer and Information Science

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SparrKULee: A Speech-evoked Auditory Response Repository of the KU Leuven, containing EEG of 85 participants

Cited by 5 publications

References 36 publications

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention

Transformer-Based Model for Auditory EEG Decoding

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