Decoding Natural Images from EEG for Object Recognition Article Swipe
Yonghao Song
,
Bingchuan Liu
,
Xiang Li
,
Nanlin Shi
,
Yijun Wang
,
Xiaorong Gao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.13234
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.13234
Electroencephalography (EEG) signals, known for convenient non-invasive acquisition but low signal-to-noise ratio, have recently gained substantial attention due to the potential to decode natural images. This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition. The framework utilizes image and EEG encoders to extract features from paired image stimuli and EEG responses. Contrastive learning aligns these two modalities by constraining their similarity. With the framework, we attain significantly above-chance results on a comprehensive EEG-image dataset, achieving a top-1 accuracy of 15.6% and a top-5 accuracy of 42.8% in challenging 200-way zero-shot tasks. Moreover, we perform extensive experiments to explore the biological plausibility by resolving the temporal, spatial, spectral, and semantic aspects of EEG signals. Besides, we introduce attention modules to capture spatial correlations, providing implicit evidence of the brain activity perceived from EEG data. These findings yield valuable insights for neural decoding and brain-computer interfaces in real-world scenarios. The code will be released on https://github.com/eeyhsong/NICE-EEG.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.13234
- https://arxiv.org/pdf/2308.13234
- OA Status
- green
- Cited By
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386237643
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386237643Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.13234Digital Object Identifier
- Title
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Decoding Natural Images from EEG for Object RecognitionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-08-25Full publication date if available
- Authors
-
Yonghao Song, Bingchuan Liu, Xiang Li, Nanlin Shi, Yijun Wang, Xiaorong GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.13234Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.13234Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2308.13234Direct OA link when available
- Concepts
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Electroencephalography, Computer science, Artificial intelligence, Decoding methods, Pattern recognition (psychology), Modalities, Speech recognition, Object (grammar), Code (set theory), Psychology, Social science, Sociology, Psychiatry, Programming language, Telecommunications, Set (abstract data type)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
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2025: 5, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.framework, | 75 |
| abstract_inverted_index.interfaces | 155 |
| abstract_inverted_index.modalities | 68 |
| abstract_inverted_index.real-world | 157 |
| abstract_inverted_index.responses. | 62 |
| abstract_inverted_index.scenarios. | 158 |
| abstract_inverted_index.Contrastive | 63 |
| abstract_inverted_index.acquisition | 7 |
| abstract_inverted_index.challenging | 99 |
| abstract_inverted_index.demonstrate | 32 |
| abstract_inverted_index.experiments | 107 |
| abstract_inverted_index.feasibility | 34 |
| abstract_inverted_index.similarity. | 72 |
| abstract_inverted_index.substantial | 15 |
| abstract_inverted_index.above-chance | 79 |
| abstract_inverted_index.constraining | 70 |
| abstract_inverted_index.non-invasive | 6 |
| abstract_inverted_index.particularly | 42 |
| abstract_inverted_index.plausibility | 112 |
| abstract_inverted_index.recognition. | 45 |
| abstract_inverted_index.comprehensive | 83 |
| abstract_inverted_index.correlations, | 133 |
| abstract_inverted_index.significantly | 78 |
| abstract_inverted_index.brain-computer | 154 |
| abstract_inverted_index.representations | 38 |
| abstract_inverted_index.self-supervised | 29 |
| abstract_inverted_index.signal-to-noise | 10 |
| abstract_inverted_index.Electroencephalography | 0 |
| abstract_inverted_index.https://github.com/eeyhsong/NICE-EEG. | 165 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |