Chun-Shu Wei

Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications.

A new adaptive equalizer for nonlinear channels

A study was made of the performance of four adaptive equalizers having a decision feedback equalizer (DFE) section for equalizing the received signal passing through a nonlinear channel. The nonlinear-DFE has simpler structure and less computations than the Volterra-DFE. Based on the simulation results, it is shown that the BER performance of a simple nonlinear-DFE is good enough compared with Volterra-DFE. The convergence of RAM-DFE is very slow and thus requires a very long training sequence. In many applications, the nonlinear-DFE is a very attractive structure for this application in channel equalization.<<ETX>>

Detection of steady-state visual-evoked potential using differential canonical correlation analysis

Steady-state visual evoked potential (SSVEP) is an electroencephalogram (EEG) activity elicited by periodic visual flickers. Frequency-coded SSVEP has been commonly adopted for functioning brain-computer interfaces (BCIs). Up to date, canonical correlation analysis (CCA), a multivariate statistical method, is considered to be state-of-the-art to robustly detect SSVEPs. However, the spectra of EEG signals often have a 1/f power-law distribution across frequencies, which inherently confines the CCA efficiency in discriminating between high-frequency SSVEPs and low-frequency background EEG activities. This study proposes a new SSVEP detection method, differential canonical correlation analysis (dCCA), by incorporating CCA with a notch-filtering procedure, to alleviate the frequency-dependent bias. The proposed dCCA approach significantly outperformed the standard CCA approach by around 6% in classifying SSVEPs at five frequencies (9-13Hz). This study could promote the development of high-performance SSVEP-based BCI systems.

Implementation of a motion sickness evaluation system based on EEG spectrum analysis

Motion sickness is a normal response to real, perceived, or even anticipated movement. People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Motion sickness occurs when the body, the inner ear, and the eyes send conflicting signals to the brain. Sensory conflict theory that came about in the 1970s has become the most widely accepted theorem of motion-sickness among scientists [1]. The theory proposed that the conflict between the incoming sensory inputs could induce motion-sickness. However, some new research studies have appeared to tackle the issue of the vestibular function in central nervous system (CNS). In the previous human subject studies, researchers attempt to confirm the brain areas involved in the conflict in multi-modal sensory systems by means of clinical or anatomical methods. Our past studies had investigated the EEG activities correlated with motion sickness in a virtual-reality based driving simulator. We found that the parietal, motor, occipital brain regions exhibited significant EEG power changes in response to vestibular and visual stimuli. Based on these experimental results, we attempt to implement an EEG-based evaluation system to estimate subjects motion sickness level upon the major EEG power spectra from these motion sickness related brain area in this study. The evaluation system can be applied to early detect the subjects motion sickness level and prevent the uncomfortable syndromes occurred in advance in our daily life.

Sigma-delta modulation adaptive digital filter

A novel approach to the realization of an adaptive transversal filter without multipliers is presented. In the realization, a modified least-mean-square (LMS) algorithm is used for updating the filter coefficients. The input signal is encoded by a sigma-delta modulator, thus it eliminates the need for complicated analog-to-digital converters and requires no multipliers for any filter length. Computer simulations are included to confirm the convergence of the mean-squared error. Performances of the filter as adaptive noise canceller and adaptive line enhancer were also demonstrated by computer simulations.<<ETX>>

An Online Brain-Computer Interface Based on SSVEPs Measured from Non-Hair-Bearing Areas.

Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas. Our previous proof-of-concept study has proposed an alternative approach that employed electroencephalographic signals collected from easily accessible non-hair-bearing areas including neck, behind the ears, and face to realize an SSVEP-based BCI. The study results showed that, with proper electrode placements and advanced signal-processing algorithms, the SSVEPs measured from non-hair-bearing areas in off-line SSVEP experiments could achieve comparable SNR to that obtained from the hair-bearing occipital areas. This study extended the previous work to systematically investigate the costs and benefits of non-hair SSVEPs. Furthermore, this study developed and evaluated an online BCI system based solely on non-hair EEG signals. A 12-target identification task was employed to quantitatively assess the performance of the online SSVEP-based BCI system. All subjects successfully completed the tasks using non-hair SSVEPs with 84.08 ± 15.60% averaged accuracy and 30.21 ± 10.61 bits/min averaged ITR. The empirical results of this study demonstrated the practicality of implementing an SSVEP-based BCI based on signals from non-hair-bearing areas, significantly improving the feasibility and practicality of real-world BCIs.

Toward non-hair-bearing brain-computer interfaces for neurocognitive lapse detection.

Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.

A Mobile Brain-Computer Interface for Freely Moving Humans

Recent advances in mobile electroencephalogram (EEG) systems fea- turing dry electrodes and wireless telemetry have promoted the applications of brain-computer interfaces (BCIs) in our daily life. In the field of neuroscience, understanding the underlying neural mechanisms of unconstrained human behaviors, i.e. freely moving humans, is accordingly in high demand. The em- pirical results of this study demonstrated the feasibility of using a mobile BCI system to detect steady-state visual-evoked potential (SSVEP) of the partici- pants during natural human walking. This study considerably facilitates the process of bridging laboratory-oriented BCI demonstrations into mobile EEG- based systems for real-life environments.

Genetic feature selection in EEG-based motion sickness estimation

Motion sickness is a common symptom that occurs when the brain receives conflicting information about the sensation of movement. Many motion sickness biomarkers have been identified, and electroencephalogram (EEG)-based motion sickness level estimation was found feasible in our previous study. This study employs genetic feature selection to find a subset of EEG features that can further improve estimation performance over the correlation-based method reported in the previous studies. The features selected by genetic feature selection were very different from those obtained by correlation analysis. Results of this study demonstrate that genetic feature selection is a very effective method to optimize the estimation of motion-sickness level. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness of individuals in real-world environments.

Transfer learning with large-scale data in brain-computer interfaces

Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data. However, current TL approaches still require training/calibration data from each of conditions, which might be difficult or expensive to obtain. This study proposed a novel TL framework that could nearly eliminate requirement of subject-specific calibration data by leveraging large-scale data from other subjects. The efficacy of this method was validated in a passive BCI that was designed to detect neurocognitive lapses during driving. With the help of large-scale data, the proposed TL approach outperformed the within-subject approach while considerably reducing the amount of calibration data required for each individual (~1.5 min of data from each individual as opposed to a 90 min pilot session used in a standard within-subject approach). This demonstration might considerably facilitate the real-world applications of BCIs.Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data. However, current TL approaches still require training/calibration data from each of conditions, which might be difficult or expensive to obtain. This study proposed a novel TL framework that could nearly eliminate requirement of subject-specific calibration data by leveraging large-scale data from other subjects. The efficacy of this method was validated in a passive BCI that was designed to detect neurocognitive lapses during driving. With the help of large-scale data, the proposed TL approach outperformed the within-subject approach while considerably reducing the amount of calibration data required for each individual (~1.5 min of data from each individual as opposed to a 90 min pilot session used in a standard within-subject approach). This demonstration might considerably facilitate the real-world applications of BCIs.