Masaki Nakanishi

Hybrid frequency and phase coding for a high-speed SSVEP-based BCI speller.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between the human brain and the external environment. Recently, multiple access (MA) methods in telecommunications have been introduced into the system design of BCIs and showed their potential in improving BCI performance. This study investigated the feasibility of hybrid frequency and phase coding methods in multi-target SSVEP-based BCIs. Specifically, this study compared two hybrid target-coding strategies: (1) mixed frequency and phase coding, and (2) joint frequency and phase coding. In a simulated online BCI experiment using a 40-target BCI speller, BCI performance for both coding approaches were tested with a group of six subjects. At a spelling speed of 40 characters per minute (1.5 seconds per character), both approaches obtained high information transfer rates (ITR) (mixed coding: 172.37±28.67 bits/min, joint coding: 170.94±28.32 bits/min) across subjects. There was no statistically significant difference between the two approaches (p>0.05). These results suggest that the hybrid frequency and phase coding methods are highly efficient for multi-target coding in SSVEP BCIs with a large number of classes, providing a practical solution to implement a high-speed BCI speller.

Enhancing detection of steady-state visual evoked potentials using individual training data

Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<;0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<;0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.

Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due to a power-law distribution of the power spectra of spontaneous electroencephalogram (EEG) signals. This study proposed a new method based on the fact that changes of canonical correlation coefficients for SSVEPs and background EEG signals follow the same trend along frequency. The proposed method defined a normalized canonical correlation coefficient, the ratio of the canonical correlation coefficient for SSVEPs to the mean of the canonical correlation coefficients for background EEG signals, to enhance the frequency detection of SSVEPs. An SSVEP dataset from 13 subjects was used for comparing classification performance between the proposed method and the standard CCA method. Classification accuracy and simulated information transfer rates (ITR) suggest that, in an unsupervised way, the proposed method could considerably improve the frequency detection accuracy of SSVEPs with little computational effort.

An approximation approach for rendering visual flickers in SSVEP-based BCI using monitor refresh rate

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize a direct communication between the human brain and the external environment in practical situations. In the conventional stimulus presentation approach, which requires a constant period of stimulation, the number of frequencies that can be presented on a computer monitor is always limited by the refresh rate of a monitor. Although an alternative approach that uses a variable on/off frame number to approximate a target flickering stimulus has been proposed in our recent study, a direct comparison between SSVEPs elicited by the conventional constant period approach and the approximation approach is still missing. This study aims to compare the amplitude, signal-to-noise ratio (SNR) and target identification accuracy of SSVEPs elicited using these two approaches with a monitor at two refresh rates (75Hz and 120Hz). Results of this study suggest that the SSVEPs elicited by the approximation approach are mostly comparable with those elicited by the constant period approach.

Integrating interference frequency components elicited by monitor refresh rate to enhance frequency detection of SSVEPs

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize a direct communication between the human brain and the outside environment in real-life situations. Recently, we proposed a stimulation approximation approach to increase the number of visual stimuli that can be realized on a computer monitor. In addition to the fundamental and harmonic frequencies, the refresh rate-based stimulation approach also elicits SSVEPs at other frequencies that are termed interference frequencies, which are derived from the interaction between the stimulation frequency and the refresh rate. This study aims to investigate properties of the interference frequency components, and propose to integrate the interference frequency components to enhance frequency detection of SSVEPs. The results suggest that the proposed approach could substantially improve the frequency detection accuracy of SSVEPs.

Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram

The effect of voluntary and involuntary eyeblinks in independent components (ICs) contributing to electroencephalographic (EEG) signals was assessed to create templates for eyeblink artifact rejection from EEG signals with small number of electrodes. Fourteen EEG and one vertical electrooculographic signals were recorded for twenty subjects during experiments that prompted subjects to blink voluntarily and involuntarily. Wavelet-enhanced independent component analysis with two markers was employed as a feature extraction scheme to investigate the effects of eyeblinks in ICs of EEG signals. Extracted features were separated into epochs and analyzed. This paper presents following characteristics: (i) voluntary and involuntary eyeblink features obtained from all channels present significant differences in the delta band; (ii) distorting effects have continued influence for 3.0-4.0s (in the occipital region, 2.0s); and (iii) eyeblink effects cease to exist after the zero-crossing four (in the occipital region, two) times, regardless of the type. Several characteristics are different between voluntary and involuntary eyeblinks in EEG signals. Therefore, any templates need both types of data for eyeblink artifact rejection if the EEG signals were obtained from small number of electrodes. Voluntary and involuntary eyeblink features obtained from all channels present significant differences in the delta band.Distorting effects have continued influence for 3.0-4.0s (in the occipital region, 2.0s).Eyeblink effects cease to exist after the zero-crossing four (in the occipital region, two) times, regardless of type.

Wheelchair control system by using electrooculogram signal processing

This study aims to propose electrooculogram signal processing method for voluntary eye blink detection and apply it to wheelchair control system. In this study, we defined double blink and wink as a voluntary eye blink, and normal blink as an involuntary blink. The proposed method can detect voluntary eye blinks in distinction from involuntary eye blinks with 98.28 percent accuracy. Additionally, we showed the effectiveness of wheelchair control system.

Periodicity detection for BCI based on periodic code modulation visual evoked potentials

In this paper, we studied the brain computer interface (BCI) based on periodic code modulation visual evoked potential (VEP). The code modulation VEP (c-VEP) is one of electroencephalogram (EEG)-based BCI methods, and can achieve high speed communication. In this method, by identifying a pseudorandom binary code (PRBC) that modulates visual stimulus from measured EEG, we can transfer the command related with the PRBC into external devices. However, the communication speed becomes slow inversely with increased number of commands. In order to solve this problem, we proposed extended c-VEP method using periodic pseudorandom binary codes. In this method, we identify the periodicity from the EEG by using autocorrelation, and the command related with periodicity of the EEG is transferred. As a result of computer simulation, we were able to detect the periodicity of the EEG. Therefore, we verified the feasibility of the periodic pseudorandom binary codes for VEP-based BCI.

Driving Control of a Powered Wheelchair by Voluntary Eye Blinking and with Environment Recognition

This paper describes the driving control system for a powered wheelchair using voluntary eye blinks. Recently, new human-computer interfaces (HCIs) that take the place of a joystick have been developed for people with disabilities of the upper body. In this paper, voluntary eye blinks are used as an HCI. However, the problem with this HCI is that the number of input directions and operations is smaller than that of a joystick, which causes inefficient movement. Therefore, assistive systems are needed for efficient and safe wheelchair movement. The proposed system is based on environment recognition and fuzzy logic. It can detect obstacles and passages, and speed and direction are calculated automatically for obstacle avoidance and right/left turns. The systems effectiveness is demonstrated through experiments with a real HCI in a real environment.

EEG analysis for acoustic quality evaluation using PCA and FDA

In this study, we propose the quantitative evaluation method of acoustic quality by using electroencephalogram (EEG). We analyze the EEG observed when subjects are listening to music by 3 kinds of stereo speaker system with different acoustic property. In this paper, as the first step of quantification, we analyze whether the difference of acoustic quality appear EEG by principal component analysis, analysis of variance and Fisher discriminant analysis. As a result, we confirmed that 1st principal component of EEG contains the imformation of acoustic quality evaluation which we can not obtain from questionnaires, and the possibility that we can use EEG for quantitative evaluation of acoustic quality.