Motor imagery (MI) based brain-computer interfaces (BCIs) have been studied as applications for the improving rehabilitation and recovery, as well as augmenting existing function. MI BCI systems typically provide feedback in an egocentric rather than an allocentric reference frame. This study aims to see if presenting stimuli in an allocentric reference frame is comparable to presenting egocentric stimuli. We used dynamic visual stimuli in egocentric and allocentric reference frames to induce motor imagery in a virtual reality (VR) environment. Eight participants imagined grasping actions with their left and right hands while observing egocentric or allocentric stimuli. The allocentric and egocentric reference frame tasks had comparable inter-rater agreement and precision, indicating that allocentric visual feedback is as effective as egocentric one for MI BCI.

Multiple stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, multiple visual stimuli are modulated with different properties such as frequencies and/or phases. However, the number of properties that can be assigned to visual stimuli rendered on a computer monitor is always limited by its refresh rate, leading to a system with limited commands or functions. To alleviate this issue, this study proposes a novel waveform-based stimulus coding method that uses modulation waveforms to differentiate resulting SSVEPs. In this paper, the discriminability of 12-class SSVEPs modulated by three types of waveforms (i.e., rectangle, sinusoidal and triangle waveforms) and four frequencies (i.e., 12, 13, 14, and 15 Hz) was investigated by computing its classification accuracy. The results showed the SSVEPs modulated by different waveforms can be successfully distinguished when using the state-of-the-art canonical correlation analysis (CCA)-based method with an average accuracy of 92.31%. This result suggests that the proposed method has great potential to significantly increase the number of functions in an SSVEP-based BCI system.

Recent studies have shown that integrating individualized templates into a template-matching target identification method could significantly improve the performance of a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). However, collecting the template (or calibration) data for each individual can be time-consuming and laborious. This issue can be alleviated by employing phase-coded visual stimuli because phase information could be discriminated by using templates synthesized from the template induced by a visual stimulus. Minimizing phase intervals between two adjacent visual stimuli could increase the number of stimuli without increasing the calibration cost. Nonetheless, no study has investigated the effects of the phase interval on the classification performance. This study compared the classification accuracy of SSVEPs with five different phase intervals (0.1 π, 0.2 π, 0.3 π, 0.4 π, and 0.5 π) using synthesized individual templates with task-related component analysis (TRCA)-based spatial filtering. From a public 12-class SSVEP dataset, phase-adjusted SSVEP data were created by adding time shifts according to the five phase intervals. The classification results showed that the accuracy was sufficiently high when the phase intervals were over 0.3 π, suggesting the use of up to six phase-shifted visual stimuli at a given frequency.

High-speed steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been developed to enable the communications between the human brain and external environments. One of the major issues in the real-world applications of SSVEP-BCIs is the laborious and time-consuming calibration process, triggering the development of transfer-learning approaches to leverage existing data from other users. A comprehensive investigation on the inter-and intra-subject variability in SSVEP data is thus needed to provide insight for designing future transfer-learning frameworks for SSVEP-BCIs. We hereby present the first study that systematically and quantitatively assesses the variability in SSVEP data, where the sources of inter-and intra-subject variability at low-and high-frequency range were identified using Fisher's discriminant ratios (FDRs). The insights gained from this work could drive the future developments of transfer-learning approaches to minimize the calibration efforts in high-speed SSVEP BCIs.

This paper presents a frequency recognition method of steady-state visual evoked potentials (SSVEPs) using binary subbands with canonical correlation analysis (CCA). The first subband contains all the target frequencies of SSVEPs. The second one includes the SSVEP signal corresponding to a desired number of higher order stimulus frequencies, which is obtained by filtering out of required range of lower order stimuli. The full dimension of artificial reference signals are used for first subband, whereas a reduced dimension of references is employed for second subband to compute canonical correlation. The weighted sum of the obtained correlation values are used to recognize the frequency of an SSVEP. The experimental results show the superiority of the proposed method compared to the state-of-the-art recognition methods.

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 3.0–4.0 s (in the occipital region, 2.0 s); 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.

Quantum computer simulators play an important role when we evaluate quantum algorithms. Quantum computation can be regarded as parallel computation in some sense, and thus, it is suitable to implement a simulator on a hardware, which can process a lot of operations in parallel. In this paper, we propose a processor architecture dedicated to simulating quantum algorithms. The proposed architecture is based on the register reordering method that shifts and swaps registers containing probability amplitudes so that the probability amplitudes of target basis states can be quickly selected. This reduces the number of large multiplexers and improves clock frequency. We implemented the processor on an FPGA. Experimental results show that the proposed processor has scalability in terms of the number of quantum bits, and can simulate quantum algorithms faster than software simulators.

Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.

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.

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 information 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.

The car transportation system, which termed as iCART (intelligent cooperative autonomous robot transporters), is developed for handling the car in the narrow space, substituting the parking and unmanning the transportation of cars. iCART is composed of two robots and these robots transport the car in coordination based on a leader-follower type distributed motion control algorithm. To avoid damaging the surface of the car, tires of the car are putted on, not grasped by, the end-effector of robots. Thus, when force/moment applied to the car by robots becomes large, the slip at the contact of the robot with the car occurs. Due to the slip, the relative position between robots is changed. In a conventional algorithm, the relative position is used for the control. Thus, after the contact of the robot with the car slips, occasionally, robots could not coordinate. For overcoming this problem, the motion control algorithm based on the caster-like motion is applied to the car transportation system iCART in this paper. Since this algorithm does not use the relative position information, the system becomes robust against the slip at the contact of the robot with the car. The proposed motion control algorithm is applied to iCART and experimental result illustrates the validity of the proposed algorithm.

Recently, the miniaturization process has brought an increase in transistor variations and in the failure rate at transistors. We propose a small variety of new standard cells. The proposed cells can correct and detect transistor faults. A functional unit with the proposed cells shows better fault tolerance. The area of this unit is approximately 1.4 times that of traditional cells.

In this paper we propose a practical framework to utilize quantum computers in the future. To the best of our knowledge, this is the first paper to show a concrete usage of quantum computation in general programming. In our framework, we can utilize a quantum computer as a coprocessor to speed-up some parts of a program that runs on a classical computer. To do so, we propose several new ideas and techniques, such as a practical method to design a large quantum circuits for search problems and an efficient quantum comparator.

Recently, reconfigurable computing has come under the spotlight as the new computing paradigm. A machine employing this paradigm combines the flexibility of a general purpose processor with the performance of a dedicated system. In this paper, we propose Event-Oriented Computing, a new application area for reconfigurable computing. We also show the architecture model suited to Event-Oriented Computing. Using Artificial Life as an example, we report the evaluation of our architecture model.

The paper describes the folding method of logic functions to reduce the size of memories for keeping the functions. The folding is based on the relation of fractions of logic functions. We show that the fractions of the full adder function have the bit-wise NOT relation and the bit-wise OR relation, and that the memory size becomes half (8-bit). We propose a new 3--1 LUT with the folding mechanisms whcih can implement a full adder with one LUT. A fast carry propagation line is introduced for a multi-bit addition. The folding and fast carry propagation mechanisms are shown to be useful to implement other multi-bit operations and general 4 input functions without extra hardware resources. The paper shows the reduction of the area consumption when using our LUTs compared to the case using 4--1 LUTs on several benchmark circuits.