- Email: [email protected]
PS-26-1 Computer-assisted interpretation of awake background EEG by use of artificial neural network adapted for individual electroencephalographer's visual interpretation
S148
IPS-25-12]
Poster session 26. EEG analysis
Programmed and not-programmed rapid arm movement sequencing. A study in normal subjects and in patients with Parkinson's disease
A. CurrY, R. Agostino, N. Modugno, G.W. Manfredi, N. Accornero, A. Berardelli. Department of Neurological Sciences,
University "La Sapienza ", Rome, Italy Parkinsonian patients are known to be slower than normal subjects in executing sequential arm movements and to exhibit progressive slowing with the progression of the motor sequence (the sequence effect). In this study the role of motor programming on the execution of motor sequences has been studied with an advance information paradigm. Ten normal and thirteen parkinsonian subjects were asked to perform sequential arm movements in two different conditions: in the first one, they did not know the sequence path before they start to more (unknown sequence) whilst in the second condition they could see the sequence path before moving (known sequence). The kinematics of the moving arm was recorded by means of the ELITE System (BTS) which is composed by a realtime T V images processor and two infrared-rays cameras able to detect the variation of the positions in the space of a reflective marker placed on the second finger of the subjects. Patients and controls performed unknown and known 5-step sequences following 6 visual targets on a screen. In the unknown condition the targets were displayed consecutively after the starting signal was give. During the known condition all the targets were displayed simultaneously on the screen before the provision of the starting signal. 1) Patients were slower than controls in executing both Unknown and Known sequences; 2) the sequence effect was present in both groups during the Unknown task, but only in the patients during the Known task; 3) both groups were faster in performing the Known than the Unknown sequences, but the percentage of T M T reduction was smaller in patients than in controls; 4) patients showed greater mean symmetry ratio of the velocity profiles than controls during both Known and Unknown sequences; 5) both patients and controls exhibited the same pattern of symmetry ratio results when comparing the first and fifth movement of both types of sequences. These results support the hypothesis that parkinsonians have more difficulties in executing movements that need to be programmed in advance than movements that can be performed in response to stimuli.
adaptability of the automatic E E G interpretation to any EEGer. A constructive artificial neural network was introduced to obtain the integrative evaluation of the E E G based on intermediate judgement of 16 items of the EEG. The feature of the neural network was that it adapted to any E E G e r who gave visual inspection for the training data. The developed method was evaluated based on the E E G data of 57 patients (37 EEGs were visually inspected by E E G e r A, and 20 EEGs were inspected by E E G e r B). The retrained neural network adapted to the E E G e r B appropriately.
IPS'26-21
Artificial neural network for EEG analysis and classification Dra~ko Furund~i6 a, Vlada Radivojevi6 2, Marko Car 2. 1 Mihajlo Pupin Institute, Knowledge Engineering Laboratory, Belgrade, Yugoslavia; 2 Institute for Mental Health, Department of Clinical Neurophysiology, Belgrade, Yugoslavia Artificial neural network (ANN) - - multilayer perception with Back Propagation (BP) learning algorithm, after the training (identification) phase, was used for: a) Elimination of artifacts by reconstruction of electrical field from signal registered from uncontaminated channels; b) Classification of E E G signals to normal and pathological; c) Automatic detection of transients. The modeling procedure of the E E G signal processing consists of three phases: 1. Selection of the optimal neural network structure; 2. Training phase - - a process of model parameter determination (ANN weight matrix) i.e. process of learning by set of learning patterns, and 3. Verification of the model - - trained A N N checking by new set of test patterns. In the training phase we used input (learning) patterns - - E E G signal sequences, which represented typical pathological E E G signals, and normal ones. At the same time scalar values 1 and 0 were used as the output (teaching) patterns. The training phase is a process of adaptation of A N N weight matrix, which minimizes the error between desired and observed outputs of the ANN. This method is easy to use on PC AT computers, and gives over 95% accurate results for the chosen set of E E G recordings.
I PS-26-31 Development of electrodes for 64-channel EEG measurement Shin'ichi Fukuzumi 1, Toshimasa Yamazaki 1, Ken'ichi Kamijo Tomoharu Kiyuna 1, Yoshiyuki Kuroiwa z. 1 Information
PS-26. EEG ANALYSIS
I PS-26-1 I Computer-assisted Interpretation of awake background EEG by use of artificial neural network adapted for Individual electroencephalographer's visual interpretation Masatoshi Nakamura 1, Yvette Chen 1, Takenao Sugi 1, Akio Ikeda 2, Hiroshi Shibasaki 2. ~Department of Electrical
Engineering, Saga Un&ersity, Japan; 2Department of Brain Pathophysiology, Kyoto University faculty of Medicine, Japan The purpose of this study is to develop a computer-assisted system for automatic E E G interpretation of the awake background EEG. Automatic E E G interpretation, consisting of quantitative E E G interpretation and E E G report making, has been developed based on the E E G data visually inspected by an E E G e r ( E E G e r A). The automatic E E G interpretation was in good agreement with the E E G e r ' s visual inspection for the E E G data which were visually inspected by the same EEGer. Current attention was paid to the
1,
Technology research Laboratories, NEC Corporation; 2 Department of Neurology, Yokohama City University School of Medicine The authors have developed a system for multi-channel E E G measurement and the E E G data analysis on personal computers and work stations. This system will enable us not only to diagnose brain functions but also to measure psychological stress. These applications require many-channel E E G data. However, it is very troublesome to simultaneously measure the enormous data. The objective of this study is to develop a 64-channel E E G electrode cap for measuring easily the multi-channel E E G data. The cap consists of 65 pairs of electrodes rings and buttons, a pair of which are used as a ground electrode, and two clip electrodes for ears as reference electrodes. These electrodes regularly distribute over the scalp. 21 electrodes are located according to the 10-20 International System and the other 43 electrodes are interpolated. To bring an electrode into contact with the scalp, each electrode button fixed a gel is fitted in the ring. The evaluation results on the cap are as follows. It takes about 30 minutes to wear all electrodes of the cap on the scalp, while it takes one hour and a half in case of normal Ag/AgC1 E E G elec-
IPS-25-12]
Poster session 26. EEG analysis
Programmed and not-programmed rapid arm movement sequencing. A study in normal subjects and in patients with Parkinson's disease
A. CurrY, R. Agostino, N. Modugno, G.W. Manfredi, N. Accornero, A. Berardelli. Department of Neurological Sciences,
University "La Sapienza ", Rome, Italy Parkinsonian patients are known to be slower than normal subjects in executing sequential arm movements and to exhibit progressive slowing with the progression of the motor sequence (the sequence effect). In this study the role of motor programming on the execution of motor sequences has been studied with an advance information paradigm. Ten normal and thirteen parkinsonian subjects were asked to perform sequential arm movements in two different conditions: in the first one, they did not know the sequence path before they start to more (unknown sequence) whilst in the second condition they could see the sequence path before moving (known sequence). The kinematics of the moving arm was recorded by means of the ELITE System (BTS) which is composed by a realtime T V images processor and two infrared-rays cameras able to detect the variation of the positions in the space of a reflective marker placed on the second finger of the subjects. Patients and controls performed unknown and known 5-step sequences following 6 visual targets on a screen. In the unknown condition the targets were displayed consecutively after the starting signal was give. During the known condition all the targets were displayed simultaneously on the screen before the provision of the starting signal. 1) Patients were slower than controls in executing both Unknown and Known sequences; 2) the sequence effect was present in both groups during the Unknown task, but only in the patients during the Known task; 3) both groups were faster in performing the Known than the Unknown sequences, but the percentage of T M T reduction was smaller in patients than in controls; 4) patients showed greater mean symmetry ratio of the velocity profiles than controls during both Known and Unknown sequences; 5) both patients and controls exhibited the same pattern of symmetry ratio results when comparing the first and fifth movement of both types of sequences. These results support the hypothesis that parkinsonians have more difficulties in executing movements that need to be programmed in advance than movements that can be performed in response to stimuli.
adaptability of the automatic E E G interpretation to any EEGer. A constructive artificial neural network was introduced to obtain the integrative evaluation of the E E G based on intermediate judgement of 16 items of the EEG. The feature of the neural network was that it adapted to any E E G e r who gave visual inspection for the training data. The developed method was evaluated based on the E E G data of 57 patients (37 EEGs were visually inspected by E E G e r A, and 20 EEGs were inspected by E E G e r B). The retrained neural network adapted to the E E G e r B appropriately.
IPS'26-21
Artificial neural network for EEG analysis and classification Dra~ko Furund~i6 a, Vlada Radivojevi6 2, Marko Car 2. 1 Mihajlo Pupin Institute, Knowledge Engineering Laboratory, Belgrade, Yugoslavia; 2 Institute for Mental Health, Department of Clinical Neurophysiology, Belgrade, Yugoslavia Artificial neural network (ANN) - - multilayer perception with Back Propagation (BP) learning algorithm, after the training (identification) phase, was used for: a) Elimination of artifacts by reconstruction of electrical field from signal registered from uncontaminated channels; b) Classification of E E G signals to normal and pathological; c) Automatic detection of transients. The modeling procedure of the E E G signal processing consists of three phases: 1. Selection of the optimal neural network structure; 2. Training phase - - a process of model parameter determination (ANN weight matrix) i.e. process of learning by set of learning patterns, and 3. Verification of the model - - trained A N N checking by new set of test patterns. In the training phase we used input (learning) patterns - - E E G signal sequences, which represented typical pathological E E G signals, and normal ones. At the same time scalar values 1 and 0 were used as the output (teaching) patterns. The training phase is a process of adaptation of A N N weight matrix, which minimizes the error between desired and observed outputs of the ANN. This method is easy to use on PC AT computers, and gives over 95% accurate results for the chosen set of E E G recordings.
I PS-26-31 Development of electrodes for 64-channel EEG measurement Shin'ichi Fukuzumi 1, Toshimasa Yamazaki 1, Ken'ichi Kamijo Tomoharu Kiyuna 1, Yoshiyuki Kuroiwa z. 1 Information
PS-26. EEG ANALYSIS
I PS-26-1 I Computer-assisted Interpretation of awake background EEG by use of artificial neural network adapted for Individual electroencephalographer's visual interpretation Masatoshi Nakamura 1, Yvette Chen 1, Takenao Sugi 1, Akio Ikeda 2, Hiroshi Shibasaki 2. ~Department of Electrical
Engineering, Saga Un&ersity, Japan; 2Department of Brain Pathophysiology, Kyoto University faculty of Medicine, Japan The purpose of this study is to develop a computer-assisted system for automatic E E G interpretation of the awake background EEG. Automatic E E G interpretation, consisting of quantitative E E G interpretation and E E G report making, has been developed based on the E E G data visually inspected by an E E G e r ( E E G e r A). The automatic E E G interpretation was in good agreement with the E E G e r ' s visual inspection for the E E G data which were visually inspected by the same EEGer. Current attention was paid to the
1,
Technology research Laboratories, NEC Corporation; 2 Department of Neurology, Yokohama City University School of Medicine The authors have developed a system for multi-channel E E G measurement and the E E G data analysis on personal computers and work stations. This system will enable us not only to diagnose brain functions but also to measure psychological stress. These applications require many-channel E E G data. However, it is very troublesome to simultaneously measure the enormous data. The objective of this study is to develop a 64-channel E E G electrode cap for measuring easily the multi-channel E E G data. The cap consists of 65 pairs of electrodes rings and buttons, a pair of which are used as a ground electrode, and two clip electrodes for ears as reference electrodes. These electrodes regularly distribute over the scalp. 21 electrodes are located according to the 10-20 International System and the other 43 electrodes are interpolated. To bring an electrode into contact with the scalp, each electrode button fixed a gel is fitted in the ring. The evaluation results on the cap are as follows. It takes about 30 minutes to wear all electrodes of the cap on the scalp, while it takes one hour and a half in case of normal Ag/AgC1 E E G elec-