Most of the feature extraction methods in existing brain-computer interfaces (BCIs) are based on the dynamic behavior of separate signals, without using the coupling information between different brain regions. In this paper, amplitude and phase coupling measures, quantified by a nonlinear regressive coefficient and phase locking value respectively, were used for feature extraction. The two measures were based on three different coupling methods determined by neurophysiological a priori knowledge, and applied to a small number of electrodes of interest, leading to six feature vectors for classification. Five subjects participated in an online BCI experiment during which they were asked to imagine a movement of either the left or right hand. The electroencephalographic (EEG) recordings from all subjects were analyzed offline. The averaged classification accuracies of the five subjects ranged from 87.4% to 92.9% for the six feature vectors and the best classification accuracies of the six feature vectors ranged between 84.4% and 99.6% for the five subjects. The performance of coupling features was compared with that of the autoregressive (AR) feature. Results indicated that coupling measures are appropriate methods for feature extraction in BCIs. Furthermore, the combination of coupling and AR feature can effectively improve the classification accuracy due to their complementarities.

A brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP) is among the fastest BCIs that have ever been reported, but it has not yet been given a thorough study. In this study, a pseudorandom binary M sequence and its time lag sequences are utilized for modulation of different stimuli and template matching is adopted as the method for target recognition. Five experiments were devised to investigate the effect of stimulus specificity on target recognition and we made an effort to find the optimal stimulus parameters for size, color and proximity of the stimuli, length of modulation sequence and its lag between two adjacent stimuli. By changing the values of these parameters and measuring classification accuracy of the c-VEP BCI, an optimal value of each parameter can be attained. Experimental results of ten subjects showed that stimulus size of visual angle 3.8°, white, spatial proximity of visual angle 4.8° center to center apart, modulation sequence of length 63 bits and the lag of 4 bits between adjacent stimuli yield individually superior performance. These findings provide a basis for determining stimulus presentation of a high-performance c-VEP based BCI system.

In an existing brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP), a method with which to increase the number of targets without increasing code length has not yet been established. In this paper, a novel c-VEP BCI paradigm, namely, grouping modulation with different codes that have good autocorrelation and crosscorrelation properties, is presented to increase the number of targets and information transfer rate (ITR). All stimulus targets are divided into several groups and each group of targets are modulated by a distinct pseudorandom binary code and its circularly shifting codes. Canonical correlation analysis is applied to each group for yielding a spatial filter and templates for all targets in a group are constructed based on spatially filtered signals. Template matching is applied to each group and the attended target is recognized by finding the maximal correlation coefficients of all groups. Based on the paradigm, a BCI with a total of 48 targets divided into three groups was implemented; 12 and 10 subjects participated in an off-line and a simulated online experiments, respectively. Data analysis of the offline experiment showed that the paradigm can massively increase the number of targets from 16 to 48 at the cost of slight compromise in accuracy (95.49% vs. 92.85%). Results of the simulated online experiment suggested that although the averaged accuracy across subjects of all three groups of targets was lower than that of a single group of targets (91.67% vs. 94.9%), the average ITR of the former was substantially higher than that of the later (181 bits/min vs. 135.6 bit/min) due to the large increase of the number of targets. The proposed paradigm significantly improves the performance of the c-VEP BCI, and thereby facilitates its practical applications such as high-speed spelling.

Computational, Mössbauer, and synchrotron radiation experiments arrive at disparate conclusions regarding the magnetic state of the high‐pressure, hexagonal closed packed, phase of iron, which likely comprises the bulk composition of Earth's inner core. Using a nonmagnetic, moissanite anvil cell together with a superconducting magnetometer, we measured the remanent magnetization of iron in response to applied magnetic fields under pressure up to 21.5 GPa at room temperature. Two independent experiments using different pressure transmission media reveal a higher remanent magnetization at 21.5 GPa than at initial conditions, which could be attributed to a distorted hexagonal closed packed phase grown during the martensitic transition. Upon both compression and decompression, the remanent magnetization of the body centered cubic phase increases several times over initial conditions while the coercivity of remanence remains mostly invariant with pressure.

Steady-state visual evoked potentials (SSVEPs) are widely employed for target detection in brain-computer interfaces (BCIs). Canonical correlation analysis (CCA), which extends ordinary correlation to two sets of variables, is a new method for SSVEP detection. In this paper, the performance of CCA is compared with that of traditional power spectral density analysis (PSDA) in terms of power spectral amplitude, recognition accuracy, information transfer rate and operating speed. The results show that the CCA method outperforms the PSDA in all these technical indexes.Keywords-brain-computer interface; steady-state visual evoked potentials; canonical correlation analysis; power spectral density analysis * Corresponding author: wqg07@163.com minimal training, and provides high transfer rate [5], it is the first choice of BCI input signals, in order to implement a practical BCI system.

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Abstract. A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.

The classification performance of a brain-computer interface (BCI) depends largely on the methods of data recording and feature extraction. The electrocorticogram (ECoG)-based BCIs are a BCI modality that has the potential to achieve high classification accuracy. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery. The optimal channel subsets are first selected by genetic algorithms from multi-channel ECoG recordings, then the power features are extracted by common spatial pattern (CSP), and finally Fisher discriminant analysis (FDA) is used for classification. The algorithm is applied to Data set I of BCI Competition III and the classification accuracy of 90% is achieved on test set by using only seven channels.