Advancing SSVEP-BCI Decoding: Cross-Subject Transfer Learning and Short Calibrated Approach with ELM-AE
The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (...
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| Published in: | 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2024; pp. 1 - 5 |
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| Language: | English |
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IEEE
01.07.2024
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| ISSN: | 2694-0604, 2694-0604 |
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| Abstract | The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (target subject). Some approaches propose linearly transforming; however, it limits the ability to capture complex and nonlinear relationships in data. This study presents a method for performing a Nonlinear Transformation (NLT) using an Extreme Learning Machine Autoencoder (ELM-AE) on SSVEP trials. To improve the NLT, it maps each trial from the existing subjects (source subjects) to one or a few templates from the target subject. This approach can enhance cross-subject recognition classification, reducing the calibration time for the target subject. Our results reported that, for one template, NLT and LST achieved 84.23% and 82.19% average recognition accuracy, respectively. Thus, our results reported that the recognition accuracy of NLT outperformed LST for all template sizes across all 35 subjects. These results demonstrated the feasibility of the NLT using one or a few templates for rapid calibration for the target subject. |
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| AbstractList | The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (target subject). Some approaches propose linearly transforming; however, it limits the ability to capture complex and nonlinear relationships in data. This study presents a method for performing a Nonlinear Transformation (NLT) using an Extreme Learning Machine Autoencoder (ELM-AE) on SSVEP trials. To improve the NLT, it maps each trial from the existing subjects (source subjects) to one or a few templates from the target subject. This approach can enhance cross-subject recognition classification, reducing the calibration time for the target subject. Our results reported that, for one template, NLT and LST achieved 84.23% and 82.19% average recognition accuracy, respectively. Thus, our results reported that the recognition accuracy of NLT outperformed LST for all template sizes across all 35 subjects. These results demonstrated the feasibility of the NLT using one or a few templates for rapid calibration for the target subject. The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (target subject). Some approaches propose linearly transforming; however, it limits the ability to capture complex and nonlinear relationships in data. This study presents a method for performing a Nonlinear Transformation (NLT) using an Extreme Learning Machine Autoencoder (ELM-AE) on SSVEP trials. To improve the NLT, it maps each trial from the existing subjects (source subjects) to one or a few templates from the target subject. This approach can enhance cross-subject recognition classification, reducing the calibration time for the target subject. Our results reported that, for one template, NLT and LST achieved 84.23% and 82.19% average recognition accuracy, respectively. Thus, our results reported that the recognition accuracy of NLT outperformed LST for all template sizes across all 35 subjects. These results demonstrated the feasibility of the NLT using one or a few templates for rapid calibration for the target subject.The Steady-State Visually Evoked Potential (SSVEP) is a robust paradigm for developing a high-speed Brain-Computer Interface (BCI). However, one of the challenges of BCI is to face the variability of EEG signals between subjects to reduce or eliminate the time calibration process for a new subject (target subject). Some approaches propose linearly transforming; however, it limits the ability to capture complex and nonlinear relationships in data. This study presents a method for performing a Nonlinear Transformation (NLT) using an Extreme Learning Machine Autoencoder (ELM-AE) on SSVEP trials. To improve the NLT, it maps each trial from the existing subjects (source subjects) to one or a few templates from the target subject. This approach can enhance cross-subject recognition classification, reducing the calibration time for the target subject. Our results reported that, for one template, NLT and LST achieved 84.23% and 82.19% average recognition accuracy, respectively. Thus, our results reported that the recognition accuracy of NLT outperformed LST for all template sizes across all 35 subjects. These results demonstrated the feasibility of the NLT using one or a few templates for rapid calibration for the target subject. |
| Author | de Carvalho, Sarah Negreiros Attux, Romis Casas, Paolo Flores, Christian |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40039415$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Accuracy Adult Algorithms Brain-Computer Interface Brain-Computer Interfaces Calibration Electroencephalography Electroencephalography - methods ELM-AE Engineering in medicine and biology Evoked Potentials, Visual - physiology Extreme learning machines Face recognition Humans Machine Learning Male Nonlinear Transformation Signal Processing, Computer-Assisted SSVEP Steady-state Target recognition Transfer learning |
| Title | Advancing SSVEP-BCI Decoding: Cross-Subject Transfer Learning and Short Calibrated Approach with ELM-AE |
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