Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

BackgroundBridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking.MethodsThis study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment.ResultsDespite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s).ConclusionsSSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications

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

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Electrophysiological Studies of Visual Attention and of Emotion Regulation

Electrophysiological methods, such as electroencephalography (EEG) and electrocardiography (ECG), measure biological activity that allow us to infer underlying cognitive processes. In the first study, we use EEG to track feature-based attention (FBA), a form of visual attention that helps one detect objects with a particular color, motion, or orientation. We explore the use of SSVEPs, generated by flicker presented peripherally, to track attention in a visual search task presented centrally. Classification results show that one can track an observer’s attended color, which suggests that these methods may provide a viable means for tracking FBA in a real-time task. In the second study, we use cardiovascular measures to examine influences of the emotion regulation strategy of reappraisal. We examine cooperation and cardiovascular responses in individuals that were defected on by their opponent in the first round of an iterated Prisoner’s Dilemma. We find significant differences between the emotion regulation conditions using the biopsychosocial (BPS) model of challenge and threat, where participants primed with the reappraisal strategy were weakly comparable with a threat state of the BPS model and participants without an emotion regulation were weakly comparable with a challenge state of the BPS model. In the third study, we use EEG to study the chromatic sensitivity of FBA for color during a visual search task. We use SSVEP responses evoked through peripheral flicker to measure the spectral tuning of color detection mechanisms and how attentional selection is affected by distractor color. Our results find smaller responses for the distractor colors and suggest that feature-based attention to a particular color involves chromatic mechanisms that both enhance the response to a target and minimize responses to distractors

Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed

Flashing color on the performance of SSVEP-based brain-computer Interfaces

A critical problem in using steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) for clinical and commercial use is the visual fatigue the user may suffer when staring at flashing stimuli. Aiming at the design of user-friendly BCIs with satisfactory performance, this work is to preliminarily investigate how different colors influence the SSVEP (i.e. frequency or phase) and system performance. The results show that white stimuli can lead to the highest performance, followed by gray, red, green and blue stimuli.published_or_final_versio