Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN
This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. The objective was to classify right hand (RH) versus right foot (RF) MI tasks in both intra- an...
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2025-01-01
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| author | Simanto Saha Mathias Baumert Alistair Mcewan |
| author_facet | Simanto Saha Mathias Baumert Alistair Mcewan |
| author_sort | Simanto Saha |
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| description | This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. The objective was to classify right hand (RH) versus right foot (RF) MI tasks in both intra- and inter-subject (pairwise and pooled) BCI settings using a 1D-CNN architecture trained on time-domain bandpass filtered electroencephalography (EEG) signals, frequency-embedded power spectral density (PSD) and cross-power spectral density (CPSD) sequences. The EEG signals were bandpass filtered with 4 Hz and 32 Hz cut-off frequencies, and PSD/CPSD sequences were estimated in the same frequency range. Thus, the number of input channels for 1D-CNN was N, N or <inline-formula> <tex-math notation="LaTeX">$N\times N$ </tex-math></inline-formula> for EEG signals, PSD or CPSD sequences. We used dataset IVa from BCI Competition III in 5−fold cross-validation settings to evaluate intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCI classification accuracies. We compared the performance of the proposed methods with classification algorithms featuring common spatial patterns (CSP) for benchmarking. The best overall classification accuracies (%) for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs were <inline-formula> <tex-math notation="LaTeX">$86.57\pm 11.69$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$70.80\pm 9.21$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$76.61\pm 12.37$ </tex-math></inline-formula> using 1D−CNN with time-domain EEG signals. The average classification accuracies using 1D-CNN with frequency-embedded PSD sequences were <inline-formula> <tex-math notation="LaTeX">$82.57\pm 10.20$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$69.32\pm 7.46$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$71.32\pm 7.96$ </tex-math></inline-formula> for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs. The proposed time/frequency feature representation techniques with 1D-CNN outperformed CSP-based algorithms (p-value <0.05). The comparative results suggest the utility of the proposed methods for MI classification, especially for a fully zero-training inter-subject BCI. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-d942286b75854a1ba3ac652d70df1e212025-08-20T04:02:18ZengIEEEIEEE Access2169-35362025-01-011313791913793010.1109/ACCESS.2025.359595311113278Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNNSimanto Saha0https://orcid.org/0000-0001-5473-3662Mathias Baumert1https://orcid.org/0000-0003-2984-2167Alistair Mcewan2https://orcid.org/0000-0001-7597-6372School of Biomedical Engineering, The University of Sydney, Sydney, AustraliaSchool of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide, AustraliaSchool of Biomedical Engineering, The University of Sydney, Sydney, AustraliaThis study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. The objective was to classify right hand (RH) versus right foot (RF) MI tasks in both intra- and inter-subject (pairwise and pooled) BCI settings using a 1D-CNN architecture trained on time-domain bandpass filtered electroencephalography (EEG) signals, frequency-embedded power spectral density (PSD) and cross-power spectral density (CPSD) sequences. The EEG signals were bandpass filtered with 4 Hz and 32 Hz cut-off frequencies, and PSD/CPSD sequences were estimated in the same frequency range. Thus, the number of input channels for 1D-CNN was N, N or <inline-formula> <tex-math notation="LaTeX">$N\times N$ </tex-math></inline-formula> for EEG signals, PSD or CPSD sequences. We used dataset IVa from BCI Competition III in 5−fold cross-validation settings to evaluate intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCI classification accuracies. We compared the performance of the proposed methods with classification algorithms featuring common spatial patterns (CSP) for benchmarking. The best overall classification accuracies (%) for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs were <inline-formula> <tex-math notation="LaTeX">$86.57\pm 11.69$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$70.80\pm 9.21$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$76.61\pm 12.37$ </tex-math></inline-formula> using 1D−CNN with time-domain EEG signals. The average classification accuracies using 1D-CNN with frequency-embedded PSD sequences were <inline-formula> <tex-math notation="LaTeX">$82.57\pm 10.20$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$69.32\pm 7.46$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$71.32\pm 7.96$ </tex-math></inline-formula> for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs. The proposed time/frequency feature representation techniques with 1D-CNN outperformed CSP-based algorithms (p-value <0.05). The comparative results suggest the utility of the proposed methods for MI classification, especially for a fully zero-training inter-subject BCI.https://ieeexplore.ieee.org/document/11113278/Brain–computer interfaceelectroencephalographymotor imagerypower spectral density1-dimensional convolutional neural network |
| spellingShingle | Simanto Saha Mathias Baumert Alistair Mcewan Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN IEEE Access Brain–computer interface electroencephalography motor imagery power spectral density 1-dimensional convolutional neural network |
| title | Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN |
| title_full | Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN |
| title_fullStr | Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN |
| title_full_unstemmed | Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN |
| title_short | Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN |
| title_sort | time domain versus frequency embedded eeg sequences for sensorimotor bci using 1d cnn |
| topic | Brain–computer interface electroencephalography motor imagery power spectral density 1-dimensional convolutional neural network |
| url | https://ieeexplore.ieee.org/document/11113278/ |
| work_keys_str_mv | AT simantosaha timedomainversusfrequencyembeddedeegsequencesforsensorimotorbciusing1dcnn AT mathiasbaumert timedomainversusfrequencyembeddedeegsequencesforsensorimotorbciusing1dcnn AT alistairmcewan timedomainversusfrequencyembeddedeegsequencesforsensorimotorbciusing1dcnn |