We aimed to develop an input interface by using the P3 component of visual event-related potentials (ERPs). When using electroencephalography (EEG) in daily applications, coping with ocular-motor artifacts and ensuring that the equipment is user-friendly are both important. To address the first issue, we applied a previously proposed method that applies an unmixing matrix to acquire independent components (ICs) obtained from another dataset. For the second issue, we introduced a 14-channel EEG commercial headset called the "Emotiv EEG Neuroheadset". An advantage of the Emotiv headset is that users can put it on by themselves within 1 min without any specific skills. However, only a few studies have investigated whether EEG and ERP signals are accurately measured by Emotiv. Additionally, no electrodes of the Emotiv headset are located over the centroparietal area of the head where P3 components are reported to show large amplitudes. Therefore, we first demonstrated that the P3 components obtained by the headset and by commercial plate electrodes and a multipurpose bioelectric amplifier during an oddball task were comparable. Next, we confirmed that eye-blink and ocular movement components could be decomposed by independent component analysis (ICA) using the 14-channel signals measured by the headset. We also demonstrated that artifacts could be removed with an unmixing matrix, as long as the matrix was obtained from the same person, even if they were measured on different days. Finally, we confirmed that the fluctuation of the sampling frequency of the Emotiv headset was not a major problem.
Respiration coaching is one of the key factors for radiation therapies. However, there are relatively few studies relating to respiration coaching, and most of them use audio or visual cues. In this paper, we show that a tactile phantom sensation moving continuously on the back can be used to adequately coach respiration timing. By using the tactile modality, the device rarely interferes with other communication channels used by therapists. The phantom sensation simplifies the mechanical structure. Several parameters were studied to obtain optimal performance when utilizing the phantom sensation. In a series of experiments, we determined the proper position and duty ratio for the actuators. To evaluate the device performance, we conducted an interference test (Kraepelin test), and the results suggest that the developed device interferes little with cognitive tasks. The experiments suggest that participants can easily understand stimulation on the back in terms of respiration guidance and properly follow changes in the cycle period with changes in respiration activity.
This paper refers to a basic study toward the goal of developing a simple and easy-to-use input interface based on P3 components of visual, event-related potentials. Because contamination from eye movements and eye blinks is a problem, a method for removing eye movement artifacts from electroencephalogram (EEG) signals by applying an independent component analysis un-mixing matrix was proposed and implemented. Input character decisions were executed using a support vector machine (SVM) for judging the P3 existence of a single stimulus. The performances were compared while varying the number of channels of EEG signals, the types of feature vectors, and the ratio of the number of data used for training the SVM. The results indicated that three EEG signal channels (Fz, Cz, Pz) were enough to remove artifacts related to eye blinks and vertical eye movements and could be used to make a decision about input characters. The number of trials necessary to decide the input characters was ten on average. The best ratio achieved for the number of training data of targets and non-targets was 1∶2. These results should be confirmed using a larger number of data sets.
The present paper refers the method to detect the degraded concentration of human who is engaged in computer work or watching television in order to find the appropriate timing for robot’s interrupt. The heart rate and respiratory measures were confirmed to change depending on the degree of concentration by an experiment. Principle component analysis was applied and two measure components were selected and rotated by the varimax method. The first principle component represented large low frequency component of heart rate variability (HRV), low respiratory frequency and large respiratory irregularity, while the second component represented high heart rate, small high frequency component of HRV. It was suggested that the first principle component can be used to discriminate between concentrated and degraded concentrated sates of human.
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