Universal EEG Encoder for Learning Diverse Intelligent Tasks
| Autor: | Viresh Gupta, Palash Aggrawal, Baani Leen Kaur Jolly, Manraj Singh Grover, Surabhi S. Nath, Rajiv Ratn Shah |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Feature extraction 02 engineering and technology Machine learning computer.software_genre External Data Representation Task (project management) Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine Encoding (memory) 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing Brain–computer interface business.industry Autoencoder Task analysis 020201 artificial intelligence & image processing Artificial intelligence business computer Encoder 030217 neurology & neurosurgery |
| Zdroj: | BigMM 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM) |
| DOI: | 10.48550/arxiv.1911.12152 |
| Popis: | Brain Computer Interfaces (BCI) have become very popular with Electroencephalography (EEG) being one of the most commonly used signal acquisition techniques. A major challenge in BCI studies is the individualistic analysis required for each task. Thus, task-specific feature extraction and classification are performed, which fails to generalize to other tasks with similar time-series EEG input data. To this end, we design a GRU-based universal deep encoding architecture to extract meaningful features from publicly available datasets for five diverse EEG-based classification tasks. Our network can generate task and format-independent data representation and outperform the state of the art EEGNet architecture on most experiments. We also compare our results with CNN-based, and Autoencoder networks, in turn performing local, spatial, temporal and unsupervised analysis on the data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|