The proposed VMD-DNN method achieved an average classification accuracy of 93% and 95.3% in subject-dependent scenarios across two datasets, outperforming baseline methods.
A novel VMD-DNN approach for single-channel SSVEP classification improves accuracy and information transfer rate in both subject-dependent and subject-independent scenarios, facilitating human-robot collaboration.
In the brain-computer interface, the SSVEP (steady-state visual evoked potential) method serves to foster collaboration between humans and robots. SSVEP-based detection methods require complex multichannel data acquisition, making them difficult to deploy due to discomfort during extended use and the complexity of the algorithms involved. On the other hand, single-channel setup offers simplicity and ease of use. However, in a single channel, achieving encouraging performance in the SD (subject-dependent) scenario is challenging, and accuracy drops further in the SI (subject-independent) scenario. This requires the development of a generalized approach to improve performance in both scenarios. This study proposes (VMD-DNN) to detect SSVEP in single-channel setups for SD and SI scenarios. The novelty of the proposed method lies in utilizing VMD (Variational Mode Decomposition) as a preprocessor, leveraging harmonic information and Kurtosis of the cross-correlation function to select harmonics from VMD decomposed signal. The preprocessed reconstructed signal uses complex spectrum features as input to the DNN for classification. The results show an average accuracy of 93%, 95.3% in SD and 79%, 92.33% in SI scenarios tested on two publicly available datasets, respectively. The ITR (Information transfer rate) was 67.50 bit/min, 92.31 bit/min for SD, and 46.13 bit/min, 85.94 bit/min for SI for both datasets, respectively. In SD, accuracy is improved by 3.34% and 5%, and ITR by 8.87% and 12.91% over baseline methods for both datasets respectively. The proposed VMD-DNN model is effective, with improved performance and lower computational complexity. The robust single-channel approach makes it user-friendly for human-robot collaboration.
Wahab et al. (Mon,) conducted a other in Brain-Computer Interface (SSVEP classification) (n=14). VMD-DNN (Variational Mode Decomposition - Deep Neural Network) vs. Baseline methods (PSDA, DWT-PSDA, EMD-PSDA, CCA-DNN, FBCCA-DNN) was evaluated on Classification accuracy in subject-dependent (SD) scenario. The proposed VMD-DNN method achieved an average classification accuracy of 93% and 95.3% in subject-dependent scenarios across two datasets, outperforming baseline methods.