期刊
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
卷 13, 期 1, 页码 3-15出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2020.2992437
关键词
Neurogaming; electroencephalography; emotiv; cognitive; affective; engagement; arousal
This study tested the performance of classifiers in neurogaming and found that a combination of classifiers is preferable over selecting a single classifier.
A growing body of literature has emerged that demonstrates the potential of neurogaming platforms for inter-facing with well-known video games. With the recent convergence of advances in consumer electronics, ubiquitous computing, and wearable sensor technologies real-time monitoring of neurocognitive and affective states can be studied in an objective manner. Whilst establishing the optimal relation among frequency bands, task engagement, and arousal states is a goal of neurogaming, a standardized method has yet to be established. Herein we aimed to test classifiers within the same context, group of participants, feature extraction methods, and protocol. Given the emphasis upon neurogaming, a commercial-grade electroencephalographic (EEG; Emotiv EPOC) headset was used to collect signals from 30 participants. The EEG data was then filtered to get separate frequency bands to train cognitive-affective classifiers with three classification techniques: Support Vector Machines (SVM), Naive Bayes (NB), and k-Nearest Neighbors (kNN). Results revealed that the NB classifier was the most robust classifier for identifying negative (e.g., character death) game-based events. The identification of general gameplay events is best identified using kNN and the Beta band. Results from this study suggest that a combination of classifiers is preferable over selection of a single classifier.
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