Summary of Multi-source Domain Adaptation with Transformer-based Feature Generation For Subject-independent Eeg-based Emotion Recognition, by Shadi Sartipi et al.
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition
by Shadi Sartipi, Mujdat Cetin
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach called Multi-Source Domain Adaptation Transformer Feature (MSDA-TF) for improving emotion recognition using EEG signals from multiple subjects. The model leverages information from various sources by retaining convolutional layers to capture shallow spatial, temporal, and spectral EEG data representations, while self-attention mechanisms extract global dependencies within these features. The adaptation process groups source subjects based on correlation values and aims to align the moments of the target subject with each source as well as within the sources. This approach is validated on the SEED dataset, showing promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding how people feel by listening to brain signals from multiple people. Right now, these computers can get confused because people’s brains work a little differently. The researchers want to find a way to make these computers more clever so they can understand what different people are feeling. They came up with an idea called MSDA-TF that uses special computer tricks to make the computers learn from many different brain signals at once. This could help computers become better at understanding human emotions. |
Keywords
* Artificial intelligence * Domain adaptation * Self attention * Transformer
