MindReader: Unsupervised Classification of Electroencephalographic Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Analyzed
2.2. Software
2.3. Architecture
- This is embarrassingly parallelizable for performance purposes;
- The computational complexity for a single channel is O(T), with T being the recording time, times O(N) with N denoting the number of channels. Even though memory consumption is low, it currently scales at O(T)*O(N), which can be further optimized, e.g., for deployment in embedded systems;
- MindReader is adaptable for different EEG montages, i.e., electrode placement;
- Identifying electrical anomalies independently allows for spatial localization per channel as well as hypothesizing the physiological relationship among different areas of the brain;
- Epileptogenic/irritative zones are potentially detectable and physically mappable. Notably, MindReader does not require specialized hardware and can be easily used after installation under any operating system: Linux, Windows, or OSX. Moreover, due to MindReader’s short run-time, it is potentially applicable in live interpretations.
3. Results
3.1. Physionet Dataset
3.2. MindReader Predictive Performance
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Rivas-Carrillo, S.D.; Akkuratov, E.E.; Valdez Ruvalcaba, H.; Vargas-Sanchez, A.; Komorowski, J.; San-Juan, D.; Grabherr, M.G. MindReader: Unsupervised Classification of Electroencephalographic Data. Sensors 2023, 23, 2971. https://doi.org/10.3390/s23062971
Rivas-Carrillo SD, Akkuratov EE, Valdez Ruvalcaba H, Vargas-Sanchez A, Komorowski J, San-Juan D, Grabherr MG. MindReader: Unsupervised Classification of Electroencephalographic Data. Sensors. 2023; 23(6):2971. https://doi.org/10.3390/s23062971
Chicago/Turabian StyleRivas-Carrillo, Salvador Daniel, Evgeny E. Akkuratov, Hector Valdez Ruvalcaba, Angel Vargas-Sanchez, Jan Komorowski, Daniel San-Juan, and Manfred G. Grabherr. 2023. "MindReader: Unsupervised Classification of Electroencephalographic Data" Sensors 23, no. 6: 2971. https://doi.org/10.3390/s23062971
APA StyleRivas-Carrillo, S. D., Akkuratov, E. E., Valdez Ruvalcaba, H., Vargas-Sanchez, A., Komorowski, J., San-Juan, D., & Grabherr, M. G. (2023). MindReader: Unsupervised Classification of Electroencephalographic Data. Sensors, 23(6), 2971. https://doi.org/10.3390/s23062971