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Quantitative analysis between visual mismatch negativity and psychopathology scale for schizophrenia
Kazuhiko Goto,Takenao Sugi,Toshihiko Maekawa,Katuya Ogata,Yoshinobu Goto,Shozo Tobimatsu,Masatoshi Nakamura 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Visual mismatch negativity (v-MMN) in electroencephalographic (EEG) record provides an index of preattentive information processing of the brain v-MMN is considered to reflect schizophrenia. The positive and negative syndrome scale (PANSS) is usually used in clinical diagnosis on patients with schizophyenia for estimating the degree of disorder. In this study, the relationship between v-MMN and PANSS was analyzed. Characteristic parameters of peals including N1, N2, P300, and MMN were established respectively. Estimated function was constructed by selected parameters. P300 was most significant characteristic related to all items of FANSS.
Automatic Sleep Stage Determination for Sleep Apnea Syndrome Patients
Bei Wang,Takenao Sugi,Fusae Kawana,Xingyu Wang,Masatoshi Nakamura 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this study, the multivariate probability distribution was investigated to develop the expert knowledge-based automatic sleep stage determination technique. The ultimate purpose is to develop adaptive automatic sleep stage deter-mination algorithm for clinical practice. Gaussi and is tribution was adopted to realize automatic parameter selection while Cauchy distribution was a dopted to estimate the parameter distriution. The different usage of multivariate probability distribution of Gaussian and Cauchy distribution presented effectiveness for automatic sleep stage determination.
Shigeto Nishida,Takenao Sugi,Akio Ikeda,Takashi Nagamine,Hiroshi Shibasaki,Masatoshi Nakamura 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
A method for detecting spikes and slow burst in photic evoked electroencephalogram (EEG) was proposed. The spikes were detected by combining methods of the morphological filter and the similarity coefficient in the time domain. The slow burst was detected by using pole of AR model in the frequency domain. The proposed method was applied for the photic evoked EEG data containing spikes and slow burst, and brought satisfactory coincidence with the results interpreted by a qualified electroencephalographer.
Recording and Characterization of EEGs by Using Wearable EEG Device
Ryo Inoue,Takenao Sugi,Yoshitaka Matsuda,Satoru Goto,Haruhiko Nohira,Ryuzo Mase 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
Electroencephalographic (EEG) record provides the state of the brain activations and is useful for the clinical diagnosis of the brain dysfunction. However, the preparation of EEG recording such as attachment of electrodes, measurement device setting, etc., is time-consuming and complex. In addition, interpretation of recorded EEG requires particular knowledge and experiences. Therefore, the use of EEG recording is limited. Recent years, simple and easy wearable EEG devices have been developed and are considered to be the practical level. Those devices are easy to attach the measurement electrodes to the scalp without special skills and do not constrain the subjects` movement. In contrast to conventional EEG recordings, the number of electrodes is limited, so the accurate interpretation and/or analysis of EEG characteristics would rather be difficult. Especially, discrimination of actual EEG activities from various contaminated artifacts are crucial for clinical application. In this study, the characteristics of the recorded EEG by using a wearable EEG device was analyzed. Automatic detection method for contaminated artifacts such eye blinking, lateral eye movement (LEM) and electromyographic (EMG) activity were developed. Results were compared with the visual inspection for the recorded data.
Construction of simple communication method by use of neuro-biological signals
Takahiro Ihara,Takenao Sugi,Makoto Eriguchi,Toyoko Asami,Masatoshi Nakamura 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Many persons suffer form severe disabilities such as armyouophic lateral selerosis (ALS) caused by neurological disorders. Such kinds of disablilities bring various restrictions to communication and circumstance in their daily life. A simple communication method using neuro-biological signal will be effective and helpful to-enhance their life quality. In this study, a simple on-off communication method using electroncephalogram(EEG) and electroculogram(EOG) was proposed. In case of EEG, parameters for linear regressive equation were selected by ATC. The results showd that the accuracy was extremely high with both communication methods.
Automatic detection of apnea and EEG arousals for sleep apnea syndrome
Genya Matsuoka,Takenao Sugi,Fusae Kawana,Masatoshi Nakamura 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Sleep apnea/hypapnea syndrome (SAHS) is one of serious sleep disorders. EEG anousals due to the a pnea hypopnea are frequently appeared in polysamnographic(PSG) record in patients with SAHS. In this study, the method for detecting EEG arousals and apnea interval in PSG record was proposed for supporting visual inspection to classify respiratory levels and EEG arousals. Two threshold values were established for classifying the sleep aprea conditions and threshold values for detecting EEG arousals were then determined accordin to the results of apnea interval detection. Proposed automatic detection method will be an effective for diagnosis support system.
Kazuhiko Goto,Takenao Sugi,Yoshitaka Matsuda,Satoru Goto,Hiroki Fukuda,Yoshinobu Goto,Takao Yamasaki,Shozo Tobimatsu 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
Visual evoked potentials (VEPs) are the electrical responses from the brain concerned with visual information processing. Amplitude of VEPs is smaller than that of background EEG activity, and the stimulus-locked averaging method is usually used for obtained the waveform. VEP response to each stimulus is not completely the same however it is varying with its amplitude and duration. Therefore, amplitude of averaged VEP waveform deteriorates due to their variability in raw data. Feature extraction of background EEG activity during visual stimulation is also a one of significant items in VEP analysis. In that case, separation of VEP component and background EEG component (mainly posterior dominant rhythm) is crucial. In the past, we proposed the method of estimating both amplitude of VEP and dominant rhythm by use of EEG model. This present study, the proposed method was applied to actual recorded VEP data and its effectiveness was evaluated. EEGs with visual stimulus were recorded from nine healthy young adults. Usefulness of the proposed method was investigated by comparing the conventional power spectrum averaging method. The proposed method will be applicable to show an accurate VEP analysis and characteristic analysis of background activity under visual stimulus.