Seo‐Hyun Lee 교수

Brain-computer interface (BCI) is oriented toward intuitive systems that users can easily operate. Imagined speech and visual imagery are emerging paradigms that can directly convey a user's intention. We investigated the underlying characteristics that affect the decoding performance of these two paradigms. Twenty-two subjects performed imagined speech and visual imagery of twelve words/phrases frequently used for patients' communication. Spectral features were analyzed with thirteen-class clas

Communication using brain-computer interface (BCI) has developed in attempts toward an intuitive system by decoding the imagined speech or visual imagery. However, discrimination between the two paradigms may be ambiguous because the user intention contains their original meaning. A clear distinction between the two paradigms may facilitate the active use of them leading to an intuitive BCI conversation system. In this study, we compared imagined speech and visual imagery in the perspective of i

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent app

Metaverse provides an alternative platform for human interaction in the virtual world. Since virtual platform holds few restrictions in changing the surrounding environments or the appearance of the avatars, it can serve as a platform that reflects human thoughts or even dreams at least in the metaverse world. When it is merged together with the current brain-computer interface (BCI) technology, which enables system control via brain signals, a new paradigm of human interaction through mind may

Every people has their own voice, likewise, brain signals dis-play distinct neural representations for each individual. Al-though recent studies have revealed the robustness of speech-related paradigms for efficient brain-computer interface, the dis-tinction on their cognitive representations with practical usabil-ity still remains to be discovered. Herein, we investigate the dis-tinct brain patterns from electroencephalography (EEG) duringimagined speech, overt speech, and speech perception in

The study investigated the level of contamination by total aerobic bacteria, coliform bacteria and food poisoning bacteria in commercial spices for the evaluation of microbiological safety. A total of 119 commercial spices was used for this study. The total aerobic bacterial count was 6.2 log CFU/g in HACCP certificated spices and 5.4 log CFU/g in HACCP non-certificated spices. Coliform bacteria were detected in 13 (35.1%) out of 37 HACCP certificated spices and 27 (32.9%) out of 82 HACCP non-ce

Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconstruct speech from neural activity. However, robust decoding of communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques still remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchroni

Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a prelimi

Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Neural representation learning based intention decoding and speech synthesis directly connects the neural activity to the means of human linguistic communication, which may greatly enhance the naturalness of communication. With the current discoveries on representation learning and the development of the speech synthesis

In Korean movies, general makeup and special makeup maximize reality and immersion as an expression of external images of a character in the movies, with the development of visual media and increasing opportunities to experience various video contents. This study aimed to identify the impact of the character image implementation elements in Korean movies with special makeup technique on viewing immersion and behavioral intention and provide a theoretical model for analyzing the movies with gener

Metaverse provides an alternative platform for human interaction in the virtual world. Since virtual platform holds few restrictions in changing the surrounding environments or the appearance of the avatars, it can serve as a platform that reflects human thoughts or even dreams at least in the metaverse world. When it is merged together with the current brain-computer interface (BCI) technology, which enables system control via brain signals, a new paradigm of human interaction through mind may

Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to reconstruct speech from neural activity. However, decoding communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-

Brain-to-speech technology represents a fusion of interdisciplinary applications encompassing fields of artificial intelligence, brain-computer interfaces, and speech synthesis. Neural representation learning based intention decoding and speech synthesis directly connects the neural activity to the means of human linguistic communication, which may greatly enhance the naturalness of communication. With the current discoveries on representation learning and the development of the speech synthesis

Visual imagery is an intuitive brain-computer interface paradigm, referring\nto the emergence of the visual scene. Despite its convenience, analysis of its\nintrinsic characteristics is limited. In this study, we demonstrate the effect\nof time interval and channel selection that affects the decoding performance of\nthe multi-class visual imagery. We divided the epoch into time intervals of 0-1\ns and 1-2 s and performed six-class classification in three different brain\nregions: whole brain, vi

Recent advances in brain-computer interface technology have shown the\npotential of imagined speech and visual imagery as a robust paradigm for\nintuitive brain-computer interface communication. However, the internal\ndynamics of the two paradigms along with their intrinsic features haven't been\nrevealed. In this paper, we investigated the functional connectivity of the two\nparadigms, considering various frequency ranges. The dataset of sixteen\nsubjects performing thirteen-class imagined spee