Non-invasive brain-computer interface (BCI) has been developed for recognizing and classifying human mental states with high performances. Specifically, classifying pilots’ mental states accurately is a critical issue because their cognitive states, which are induced by mental fatigue, workload, and distraction, may be fundamental in catastrophic accidents. In this study, we present an electroencephalogram (EEG) classification of four mental states (fatigue, workload, distraction, and the normal state) from EEG signals in both offline and pseudo-online analyses. To the best of our knowledge, this study is the first attempt to classify pilots’ mental states using only EEG signals during continuous decoding. We recorded EEG signals from seven pilots under various simulated flight conditions. We proposed a multiple feature block-based convolutional neural network (MFB-CNN) with temporal-spatio EEG filters to recognize the pilot’s current mental states. We validated the proposed method for two analyses across all subjects. In the offline analysis, we confirmed the classification accuracy of 0.75 (±0.04). Also, in the pseudo-online analysis, we obtained the detection accuracy of 0.72 (±0.20), 0.72 (±0.27), and 0.61 (±0.18) for fatigue, workload, and distraction, respectively. Hence, we demonstrate the feasibility of classifying various types of mental states for implementation in real-world environments.

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.

A new kind of sequence–to–sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG–based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery–based brain–computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi–head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.

Brain–computer interface (BCI) is a technology that allows users to control computers by reflecting their intentions. Electroencephalogram (EEG)–based BCI has been developed because of its potential, however, its decoding performance is still insufficient to apply in the real–world environment. As deep learning methods achieve the significant performance in various domains, it has been applied in the EEG–based BCI domain. In particular, ShallowConvNet is one of the most widely used methods because of its robust decoding performance in multiple datasets. However, the model’s parameters have to be optimized to apply this model to various datasets each time, and we have also found some issues in architecture that disturb the stable training. In this paper, we highlight potential problems that might arise in ShallowConvNet and investigate the potential solutions. In addition, we propose a novel model, called M–ShallowConvNet, which solves the existing problems. The proposed model achieves the accuracies of 0.8164 and 0.8647 in datasets 2a and 2b of BCI Competition IV, respectively. Hence, we demonstrate that performance improvement can be achieved with only a few small modifications that resolve the problems of the conventional model.

Recent advances in brain-computer interface (BCI) techniques have led to increasingly refined interactions between users and external devices. Accurately decoding kinematic information from brain signals is one of the main challenges encountered in the control of human-like robots. In particular, although the forearm of an upper extremity is frequently used in daily life for high-level tasks, only few studies addressed decoding of the forearm movement. In this study, we focus on the classification of forearm movements according to elaborated rotation angles using electroencephalogram (EEG) signals. To this end, we propose a hierarchical flow convolutional neural network (HF-CNN) model for robust classification.We evaluate the proposed model not only with our experimental dataset but also with a public dataset (BNCI Horizon 2020). The grand-average classification accuracies of three rotation angles yield 0.73 (±0.04) for the motor execution (ME) task and 0.65 (±0.09) for the motor imagery (MI) task across ten subjects in our experimental dataset. Further, in the public dataset, the grand-averaged classification accuracies were 0.52 (±0.03) for ME and 0.51 (±0.04) for MI tasks across fifteen subjects. Our experimental results demonstrate the possibility of decoding complex kinematics information using EEG signals. This study will contribute to the development of a brain-controlled robotic arm system capable of performing high-level tasks.

Detection of the pilots’ mental states is particularly critical because their abnormal mental states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility of classifying the various specific AbSs (namely, low fatigue, high fatigue, low workload, high workload, 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 multiple AbSs of pilots. We proposed the hybrid deep neural networks with five convolutional blocks and two long short-term memory layers for decoding multiple AbSs. We designed the model to extract the informative features from electroencephalography signals. A total of ten pilots conducted the experiment in a simulated flight environment. Compared with five conventional models, our proposed model achieved the highest grand-average accuracy of 68.04 $(\pm$ 5.26)% which is at least 6.55% higher than other conventional models for classifying seven mental states across all subjects. Our proposed model could distinguish and classify low and high levels for each status category and give appropriate feedback to the subjects. In addition, we found nine indicators that showed the statistically significant differences between two mental states ( p $<$ 0.05). Hence, we believe that it will contribute significantly to autonomous driving or autopilot advances based on artificial intelligence technology in the future.