METHOD FOR TRAINING TOP-DOWN SELECTIVE ATTENTION IN ARTIFICIAL NEURAL NETWORKS

본 발명은 인간의 두뇌에서 일어나는 선택적 주의 집중의 능력을 공학적으로 구현하고, 이를 인식기에 적용하여 정밀도를 임의로 높이기 위해 다층 퍼셉트론 네트워크 기반 인공 신경망의 학습 수행을 통해 획득된 시냅스별 연결 강도에 대응하는 복수의 가중치를 기설정된 가중치 값을 기반으로 고정하고, 다수의 뉴런으로 구성된 입력층에 훈련 패턴을 제시하여 훈련 패턴에 대응되는 인공 신경망 내의 연산 을 수행하고 복수의 도메인별 데이터에 대응하는 입력 벡터에 대한 출력을 산출한 후 산출된 출력을 통해 도메인별 정책 기반 데이터 인식률을 비교하여 인식률이 가장 높은 데이터의 해당 출력에 가중치를 부여하여 하향식(top-down) 선택적 주의집중 기반 시냅스별 트레이닝을 수행함으로써 복수의 후보자 클래스에 대하여 주의 집중의 정도를 새로운 인식 척도로 정의하여 기존의 하나의 후보자 클래스에 대한 인식시스템에 비하여 우수한 인식 결과를 출력 가능할 뿐만 아니라, 가중합을 통해 구현 가능한 최고 속도를 떨어뜨리지 않고 연산의 정밀도(precision)를 임의로 높일 수 있고, 이러한 하향식 선택적 주의 집중의 다층 퍼셉트론은 생물학적으로 선택적 주의집중의 메커니즘을 모델링하는 것과 동시에 이를 이용하여 대용량 범용 신경망 컴퓨터의 구현이 가능할 뿐만 아니라 소형 반도체에도 집적이 가능하여 다양한 인공 신경망 응용 분야에 적용 가능한 기술을 제공하고자 한다

뇌 신호처리에 기반한 인간의 내적 상태 이해 연구

학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2016.2 ,[x, 103 p. :]agreement/disagreement to others, and trustworthiness of others. In this dissertation, we proposed an implicit intention recognition framework to classify a user’s im-plicit agreement/disagreement at his/her EEG single trial level. From EEG data recorded during self-relevant sentence reading, we were able to discriminate two implicit intentions, i.e., ‘agreement’ and ‘disagreement.’ To improve the classification accuracy, discriminant features were selected based on Fisher score among EEG frequency bands and electrodes. Especially, the time-frequency representation with Morlet wavelet trans-forms showed clear differences in gamma, beta, and alpha band powers at fronto-central area, and theta band power at centro-parietal area, where were also found in an fMRI study. The best classification accuracy of 75.5% was obtained by a support vector machine (SVM) classifier with the gamma band features at fron-to-central area. This result may enable a new intelligent user-interface which understands human internal states regarding agreement and disagreement. We designed another experiment to search for the second human internal state, i.e., trustworthiness. Specifically, when a human and an intelligent machine work together as a team, human trust has been broadly known as its strong influence to the performance. Yet, an electrophysiological signature of trust has not been isolated. In order to isolate such a signature, we recorded event-related potentials while healthy sub-jects (N = 31) played a theory-of-mind game with two types of computerized agents: with or without human-like cues. Electrophysiological activities in brain regions belonging to the theory-of-mind network correlated with perceived capability, especially when a machine opponent has some human-likeness. In particular, our research shows that activity in the left parietal region correlating with a human player’s future behavior can be identified as the neural signature of capability-based trust. These results reveal that brain signals underly-ing trust as influenced by perceived capability and human-likeness might be useful for performance optimi-zation of human-machine systems. According to the results in two studies, we proposed novel research paradigms to understand human internal states, and successfully showed relationship between human internal states and neural responses in a human brain. By understanding a human brain, we believe that it is possible to develop a better human ma-chine interface which can support human beings.Understanding human minds is a challenging goal for a successful interaction between a human and a machine. Although the erstwhile machines were trained and developed to understand explicitly presented human minds, understanding un-presented or hidden human mind will play an important role for a future human-machine interface. To understand un-presented human mind, we hypothesized that the space of hu-man internal states has several independent axes, for example, memory, emotion, intention, trustworthiness, etc. Each state may be represented on its axes in a form of neural responses in a human brain. Therefore, it is necessary to investigate brain signals in a human brain representing his/her internal state. In the same vein, a brain-computer interface (BCI) has been developed to facilitate a communication between a human and a machine. It has primarily been applied in healthcare to assist physically impaired patients, who have a diffi-culty to present their mind. For a general purpose, a future BCI should assist healthy people in their natural daily life. Our continuous efforts resulted in a good success of understanding two different human internal states한국과학기술원 :전기및전자공학부

EEG에 기반한 묵시적 의도 파악 연구

학위논문(석사) - 한국과학기술원 : 전기 및 전자공학과, 2011.8, [ v, 50 p. ]There are many approaches that understand human intention. Although previous study has mainly used behavioral data which are explicitly expressed such as speech, gesture, and touch strokes, but human intention cannot be revealed explicitly in real life. Whether it is intended or by accident, there are many situations that we do not disclose our minds. In this study, EEG was examined to investigate the human implicit intention. Subjects showed their intention by their voices whether they agree or disagree toward given obvious and non-obvious sentences. Experiment focuses on some situation that human may not want to express their real intention to others and may answer differently. It is assumed that brain activation may not be the same when they show differently with their real intention, so it can be a measurement of the implicit intention. ICA is applied to extract independent components of the recorded EEG data. And only few components were selected based on Fisher Linear Discriminant (FLD) which can discriminate between agreement and disagreement state. Using selected components, support vector machine trained with obvious condition identified the validation sample from the classifier output. The results showed that SVM output of selected independent components can discriminate implicit intention states, and recognize non-obvious condition. It may be used to understand implicit intention.한국과학기술원 : 전기 및 전자공학과