Personalised Affective Classification Through Enhanced EEG Signal Analysis
Background and Objectives Declining mental health is a prominent and concerning issue. Affective classification, which employs machine learning on brain signals captured from electroencephalogram (EEG), is a prevalent approach to address this issue. However, many existing studies have adopted a one-...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2025.2450568 |
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| Summary: | Background and Objectives Declining mental health is a prominent and concerning issue. Affective classification, which employs machine learning on brain signals captured from electroencephalogram (EEG), is a prevalent approach to address this issue. However, many existing studies have adopted a one-size-fits-all approach, where data from multiple individuals are combined to create a single “generic” classification model. This overlooks individual differences and may not accurately capture the unique emotional patterns of each person.Methods This study explored the performance of six machine learning algorithms in classifying a benchmark EEG dataset (collected with a MUSE device) for affective research. We replicated the best performing models on the dataset found in the literature and present a comparative analysis of performance between existing studies and our personalised approach. We also adapted another EEG dataset (commonly called DEAP) to validate the personalised approach. Evaluation was based on accuracy and significance test using McNemar statistics. Model runtime was also used as an efficiency metric.Results The personalised approach consistently outperformed the generalised method across both datasets. McNemar’s test revealed significant improvements in all but one machine learning algorithm. Notably, the Decision Tree algorithm consistently excelled in the personalised mode, achieving an accuracy improvement of 0.85% ([Formula: see text]) on the MUSE dataset and a 4.30% improvement on the DEAP dataset, which was also statistically significant ([Formula: see text]). Both Decision Tree models were more efficient than their generalised counterpart with 1.270 and 23.020-s efficiency gain on the MUSE and DEAP datasets, respectively.Conclusions This research concludes that smaller, personalised models are a far more effective way of conducting affective classification, and this was validated with both small (MUSE) and large (DEAP) datasets consisting of EEG samples from 4 to 32 subjects, respectively. |
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| ISSN: | 0883-9514 1087-6545 |