Korea University · Neuroscience
Professor Wonjun Ko's research lab specializes in advancing brain-computer interface (BCI) systems through deep learning and signal processing techniques, with a strong focus on electroencephalography (EEG) signal analysis. The lab explores innovative deep neural network architectures—particularly convolutional and reinforcement learning-based models—that enhance feature representation, classification accuracy, and interpretability of EEG data. Key research directions include semi-supervised and adversarial learning for data-efficient BCI, real-time driver fatigue detection, and neurophysiologically interpretable models for motor imagery classification. The lab emphasizes practical applications in human-computer interaction, traffic safety, and personalized BCI systems with minimal calibration requirements.
Figures are computed from collected data and may differ slightly.
Recent advances in deep learning have had a methodological and practical impact on brain-computer interface (BCI) research. Among the various deep network architectures, convolutional neural networks (CNNs) have been well suited for spatio-spectral-temporal electroencephalogram (EEG) signal representation learning. Most of the existing CNN-based methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and involve densely conne
Estimating driver fatigue is an important issue for traffic safety and user-centered brain-computer interface. In this paper, based on differential entropy (DE) extracted from electroencephalography (EEG) signals, we develop a novel deep convolutional neural network to detect driver drowsiness. By exploiting DE of EEG samples, the proposed network effectively extracts class-discriminative deep and hierarchical features. Then, a densely-connected layer is used for the final decision making to ide
Brain-computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has
In this paper, we propose a novel architecture of a deep neural network for EEG-based motor imagery classification. Unlike the existing deep neural networks in the literature, the proposed network allows us to analyze the learned network weights from a neurophysiological perspective, thus providing an insight into the underlying patterns inherent in motor imagery induced EEG signals. In order to validate the effectiveness of the proposed method, we conducted experiments on the BCI Competition IV
In this article, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single electroencephalogram (EEG) trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning-based brain–computer interface methods. To be specific, we devise an actor–critic network such that an agent can determine which timepoints need to be used (informative) or discarded (uninformative
Recent advances in deep learning have made a progressive impact on BCI researches. In particular, convolutional neural networks (CNNs) with different architectural forms have been studied for spatio-temporal or spatio-spectral feature representation learning. However, there still remain many challenges and limitations due to the necessity of a large annotated training samples for robustness. In this paper, we propose a semi-supervised deep adversarial learning framework that effectively utilizes
Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learning methods in BCIs. We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with real sam
Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have attracted widespread attention for monitoring the clinical condition of users and identifying their intention/emotion. Nevertheless, the existing methods generally model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, and thus represent complex spectro-/spatiotemporal patterns and suffer from high variability. In this work, we propose the novel EEG-oriented
Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have gained widespread attention to monitor a user's clinical condition or identify his/her intention/emotion. Nevertheless, the existing methods mostly model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, thus suffering from representing complex spatio-spectro-temporal patterns as well as inter-subject variability. In this work, we propose novel EEG-oriented s
Recent advances in deep learning have had a methodological and practical impact on brain-computer interface research. Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal electroencephalogram signal representation learning. Most of the existing CNN-based methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and involve densely connected layers for cla
Recently, deep learning, a branch of machine learning and data mining, has gained widespread acceptance in many applications thanks to its unprecedented successes. In this regard, pioneering studies employed deep learning frameworks for imaging genetics in virtue of their own representation caliber. But, existing approaches suffer from some limitations: (i) exploiting a simple concatenation strategy for joint analysis, (ii) a lack of extension to biomedical applications, and (iii) insufficient a
Recently, deep learning frameworks have gained increasing attentions from electroencephalogram (EEG)-based driver’s fatigue estimation, thanks to their unprecedented feature extraction calibre. However, it is still challenging to develop session- and/or subject-independent system, because of the complex structural characteristics of EEG signals. In this regard, this work proposes a novel deep convolutional neural network architecture that can learn spectro-spatio-temporal representation of the v
In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods. To be specific, we devise an actor-critic network such that an agent can determine which timepoints need to be used (informative) or discarded (uninformative) in composing the intention-related features i
Abstract Convolutional neural networks (CNNs), which can recognize structural/configuration patterns in data with different architectures, have been studied for feature extraction. However, challenges remain regarding leveraging advanced deep learning methods in BCIs. We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with
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