An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks
This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area...
| 出版年: | Brain Sciences |
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| 主要な著者: | , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
MDPI AG
2024-08-01
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| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/2076-3425/14/8/836 |
| _version_ | 1850372238897840128 |
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| author | Brian Ezequiel Ail Rodrigo Ramele Juliana Gambini Juan Miguel Santos |
| author_facet | Brian Ezequiel Ail Rodrigo Ramele Juliana Gambini Juan Miguel Santos |
| author_sort | Brian Ezequiel Ail |
| collection | DOAJ |
| container_title | Brain Sciences |
| description | This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain–computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI). |
| format | Article |
| id | doaj-art-e49acd581ca04a14a22b7e71e1040f5e |
| institution | Directory of Open Access Journals |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-e49acd581ca04a14a22b7e71e1040f5e2025-08-19T23:00:52ZengMDPI AGBrain Sciences2076-34252024-08-0114883610.3390/brainsci14080836An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural NetworksBrian Ezequiel Ail0Rodrigo Ramele1Juliana Gambini2Juan Miguel Santos3Instituto Tecnológico de Buenos Aires (ITBA), Buenos Aires C1437, ArgentinaInstituto Tecnológico de Buenos Aires (ITBA), Buenos Aires C1437, ArgentinaCentro de Investigación en Informática Aplicada (CIDIA), Universidad Nacional de Hurlingham (UNAHUR), Hurlingham B1688, ArgentinaCentro de Investigación en Informática Aplicada (CIDIA), Universidad Nacional de Hurlingham (UNAHUR), Hurlingham B1688, ArgentinaThis work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain–computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).https://www.mdpi.com/2076-3425/14/8/836XAIBCIEEGP300ALSwaveform |
| spellingShingle | Brian Ezequiel Ail Rodrigo Ramele Juliana Gambini Juan Miguel Santos An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks XAI BCI EEG P300 ALS waveform |
| title | An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks |
| title_full | An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks |
| title_fullStr | An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks |
| title_full_unstemmed | An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks |
| title_short | An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks |
| title_sort | intrinsically explainable method to decode p300 waveforms from eeg signal plots based on convolutional neural networks |
| topic | XAI BCI EEG P300 ALS waveform |
| url | https://www.mdpi.com/2076-3425/14/8/836 |
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