Toward Adaptive BCIs: Enhancing Decoding Stability via User State-Aware EEG Filtering

Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user attention fluctuates, brain states shift over time, or irregular artifacts appear during interaction. To mitigate these issues, we introduce a user state-aware electroencephalogram (EEG) filtering framework that refines neural representations before decoding user intentions. The proposed method continuously estimates the user's cognitive state (e.g., focus or distraction) from EEG features and filters unreliable segments by applying adaptive weighting based on the estimated attention level. This filtering stage suppresses noisy or out-of-focus epochs, thereby reducing distributional drift and improving the consistency of subsequent decoding. Experiments on multiple EEG datasets that emulate real BCI scenarios demonstrate that the proposed state-aware filtering enhances classification accuracy and stability across different user states and sessions compared with conventional preprocessing pipelines. These findings highlight that leveraging brain-derived state information--even without additional user labels--can substantially improve the reliability of practical EEG-based BCIs.

Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms

In this paper, we propose a conceptual framework for personalized brain-computer interface (BCI) applications, which can offer an enhanced user experience by customizing services to individual preferences and needs, based on endogenous electroencephalography (EEG) paradigms including motor imagery (MI), speech imagery (SI), and visual imagery. The framework includes two essential components: user identification and intention classification, which enable personalized services by identifying individual users and recognizing their intended actions through EEG signals. We validate the feasibility of our framework using a private EEG dataset collected from eight subjects, employing the ShallowConvNet architecture to decode EEG features. The experimental results demonstrate that user identification achieved an average classification accuracy of 0.995, while intention classification achieved 0.47 accuracy across all paradigms, with MI demonstrating the best performance. These findings indicate that EEG signals can effectively support personalized BCI applications, offering robust identification and reliable intention decoding, especially for MI and SI.

CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals

Brain-computer interface (BCI) is a communication system between humans and computers reflecting human intention without using a physical control device. Since deep learning is robust in extracting features from data, research on decoding electroencephalograms by applying deep learning has progressed in the BCI domain. However, the application of deep learning in the BCI domain has issues with a lack of data and overconfidence. To solve these issues, we proposed a novel data augmentation method, CropCat. CropCat consists of two versions, CropCat-spatial and CropCat-temporal. We designed our method by concatenating the cropped data after cropping the data, which have different labels in spatial and temporal axes. In addition, we adjusted the label based on the ratio of cropped length. As a result, the generated data from our proposed method assisted in revising the ambiguous decision boundary into apparent caused by a lack of data. Due to the effectiveness of the proposed method, the performance of the four EEG signal decoding models is improved in two motor imagery public datasets compared to when the proposed method is not applied. Hence, we demonstrate that generated data by CropCat smooths the feature distribution of EEG signals when training the model.

Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals

Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly critical because their abnormal mental states could cause catastrophic accidents. In this study, we presented the feasibility of classifying distraction levels (namely, normal state, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attempt to classify distraction levels under a flight environment. We proposed a model for classifying distraction levels. A total of ten pilots conducted the experiment in a simulated flight environment. The grand-average accuracy was 0.8437 for classifying distraction levels across all subjects. Hence, we believe that it will contribute significantly to autonomous driving or flight based on artificial intelligence technology in the future.