Conventional semi-supervised learning (SSL) ideally assumes that labeled and unlabeled data share an identical class distribution; however, in practice, this assumption is easily violated, as unlabeled data often includes unknown class data, i.e., outliers. The outliers are treated as noise, considerably degrading the performance of SSL models. To address this drawback, we propose a novel framework, diversify and conquer (DAC), to enhance SSL robustness in the context of open-set SSL (OSSL). In particular, we note that existing OSSL methods rely on prediction discrepancies between inliers and outliers from a single model trained on labeled data. This approach can be easily failed when the labeled data are insufficient, leading to performance degradation that is worse than naive SSL that do not account for outliers. In contrast, our approach exploits prediction disagreements among multiple models that are differently biased toward the unlabeled distribution. By leveraging the discrepancies arising from training on unlabeled data, our method enables robust outlier detection, even when the labeled data are underspecified. Our key contribution is constructing a collection of differently biased models through a single training process. By encouraging divergent heads to be differently biased toward outliers while making consistent predictions for inliers, we exploit the disagreement among these heads as a measure to identify unknown concepts. Extensive experiments demonstrate that our method significantly surpasses state-of-the-art OSSL methods across various protocols.

Brain-computer interface (BCI) technology enables communication between humans and devices by reflecting users' status and intentions. Electroencephalography (EEG) signals are utilized to capture brain electrical activity with no surgical operation. When conducting motor imagery (MI), one of the endogenous BCI paradigms, the users imagine the movement of muscles used when performing a certain movement without actual physical movement. However, not all subjects show outstanding classification performance in decoding MI-based EEG signals. We propose the novel method that utilizes the weights of the pre-trained model to generate the personalized weights, effectively combining the general MI features with the personalized features. We used the 5-fold cross-validation for evaluating the performances, and conducted the experiments in 3 different pre-trained models (Top-3, Top-5, and Top-7). We compared the performances of our proposed method using the baseline and the full fine-tuning. In comparison to our proposed method with the baseline, our proposed method achieved the improvement of the average accuracies in all pre-trained models, and those values were 0.123, 0.138, and 0.143, respectively. When comparing our proposed method with the full fine-tuning, the average accuracies of our proposed method were the highest in all pre-trained models, and the differences in the average accuracies were 0.012, 0.019, and 0.009, respectively. Hence, we demonstrated the possibility of improving the precision and effectiveness of the EEG-based systems by reflecting the individual differences in EEG signals among the subjects with low classification accuracy.

Brain-computer interface (BCI) enables the communication between humans and devices by reflecting humans' intentions and status. Endogenous BCI is the imagined-based BCI and it has the advantage that the fatigue level of the body, especially the eyes, is relatively low and no additional equipment for offering stimulation is required. When conducting imagined speech, one of the endogenous BCI paradigms, the users imagine the pronunciation as if actually speaking. In contrast, overt speech is that the users directly pronounce the words. We proposed the transfer learning-based method from overt speech- to imagined speech-based electroencephalogram (EEG) signals (TOINet). The proposed method utilizes an encoder to extract the feature vector of imagined speech from EEG signals, which is subsequently reconstructed into overt speech signals using the decoder. Through this process, the model can identify the significant and common features present in EEG signals for both overt and imagined speech, facilitating the classification of EEG signals associated with imagined speech. Eight subjects participated in the experiment. The average accuracy of the TOINet was 0.4841 for classifying four words and the EEG features of overt speech improved the performance by 0.0742. Hence, we demonstrated that EEG features of overt speech could improve the decoding performance of imagined speech.

Brain-computer interface (BCI) is a technology that controls computers by reflecting users’ intentions. Especially the electroencephalogram (EEG)-based BCI systems have been developed because of their potential utility. In BCI studies, controlling the drone swarm is one of the important issues since it improves work efficiency and safety. Also, current research has investigated how the drone swarms are controlled by imagining their formations using visual imagery (VI)-based EEG signals. The raw EEG signals and the spectrogram are widely used as input representations for decoding EEG signals. However, the decoding performance of the VI-based EEG signals is low to control the drone swarm due to noise in the raw EEG signals and information loss problems that may arise in the spectrogram. In this paper, we develop the EEGgram generator that extracts spectrogram-like features from the raw EEG signals minimizing information loss problems. Also, we propose the EEGgramNet, which could extract the significant information from VI-based EEG signals using both the spectrogram and the EEGgram as inputs. The proposed method outperforms an accuracy of 0.643, which is 8.4 % higher than that of the best conventional method. Hence, we demonstrate the possibility of constructing a VI-based BCI system to control the drone swarm by imagining its formations.

Brain-computer interface (BCI) is used for communication between humans and devices by recognizing humans’ status and intention. Communication between humans and a drone using electroencephalogram (EEG) signals is one of the most challenging issues in the BCI domain. In particular, the control of drone swarms (the direction and formation) has more advantages compared to the control of a drone. The visual imagery (VI) paradigm is that subjects visually imagine specific objects or scenes. Reduction of the variability among subjects’ EEG signals is essential for practical BCI-based systems. In this study, we proposed the subepoch-wise feature encoder (SEFE) to improve the performances in the subject-independent tasks by using the VI dataset. This study is the first attempt to demonstrate the possibility of generalization among subjects in the VI-based BCI. We used the leave-one-subject-out cross-validation for evaluating the performances. We obtained higher performances when including our proposed module than excluding our proposed module. The DeepConvNet with SEFE showed the highest performance of 0.72 among six different decoding models. Hence, we demonstrated the feasibility of decoding the VI dataset in the subject-independent task with robust performances by using our proposed module.