Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning (2405.02422v2)
Abstract: In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant's attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80\% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78\% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future.
- A brain-based general measure of attention. Nature human behaviour, 6(6):782–795, 2022.
- The self-regulating brain and neurofeedback: Experimental science and clinical promise. cortex, 74:247–261, 2016.
- Brain computer interfaces, a review. sensors, 12(2):1211–1279, 2012.
- A comprehensive review of eeg-based brain–computer interface paradigms. Journal of neural engineering, 16(1):011001, 2019.
- Home-based brain–computer interface attention training program for attention deficit hyperactivity disorder: A feasibility trial. Child and Adolescent Psychiatry and Mental Health, 17(1):1–11, 2023.
- Intracerebral electrophysiological recordings to understand the neural basis of human face recognition. Brain Sciences, 13(2):354, 2023.
- Real-time decoding of attentional states using closed-loop eeg neurofeedback. Neural Computation, 33(4):967–1004, 2021.
- Attention to faces modulates early face processing during low but not high face discriminability. Attention, Perception, & Psychophysics, 71:837–846, 2009.
- Pattern classification of eeg signals reveals perceptual and attentional states. PloS one, 12(4):e0176349, 2017.
- Unicorn the brain interface. https://www.unicorn-bi.com. Accessed: 2024-01-31.
- Decoding attentional state to faces and scenes using eeg brainwaves. Complexity, 2019, 2019.
- Oops!’: performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35(6):747–758, 1997.
- The hippocampus reevaluated in unconscious learning and memory: at a tipping point? Frontiers in human neuroscience, 6:80, 2012.
- Massively parallel non-stationary eeg data processing on gpgpu platforms with morlet continuous wavelet transform. Journal of Internet Services and Applications, 3(3):347–357, 2012.
- Comparative study of k-nn, naive bayes and svm for face expression classification techniques. Balkan Journal of Interdisciplinary Research, 9(3):23–32, 2023.
- Differences in learning characteristics between support vector machine and random forest models for compound classification revealed by shapley value analysis. Scientific Reports, 13(1):5983, 2023.
- Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2623–2631, 2019.
- Evaluation of hyperparameter optimization in machine and deep learning methods for decoding imagined speech eeg. Sensors, 20(16):4629, 2020.
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