Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN
Open accessAbstract
Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of brain-computer Interface applications.
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Ref. [251] employed CNN for feature extraction and a reinforced selective attention model to automatically choose the most characteristic information from multiple channels.
“Ref. [251] employed CNN for feature extraction and a reinforced selective attention model to automatically choose the most characteristic information from multiple channels.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [251]
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Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
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“[333] employed CNN for feature extraction and a reinforced selective attention model to automatically choose the most characteristic information from multiple channels.”
MethodSelf-citation
- Authors
- Zhang, X., Yao, L., Wang, X., Zhang, W., Zhang, S., & Liu, Y.
- Affiliations
- UNSW Sydney (AU)
- University of Technology Sydney (AU)
- Michigan State University (US)
- Venue
- 2019 IEEE International Conference on Data Mining (ICDM)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 896-905
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W3003385856
- Cited by
- 22
- References
- 40
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 3
- FWCI
- 3.46
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Proceedings (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- adaptive feature selection, EEG motor imagery, intent classification, personal identification, neurological diagnosis, brain-computer interface, low signal-to-noise ratio
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