Neural Dynamics of Target Detection via Wireless EEG in Embodied Cognition.

Embodied cognitive attention detection is important for many real-world applications, such as monitoring attention in daily driving and studying. Exploring how the brain and behavior are influenced by visual sensory inputs becomes a major challenge in the real world. The neural activity of embodied mind cognitive states can be understood through simple symbol experimental design. However, searching for a particular target in the real world is more complicated than during a simple symbol experiment in the laboratory setting. Hence, the development of realistic situations for investigating the neural dynamics of subjects during real-world environments is critical. This study designed a novel military-inspired target detection task for investigating the neural activities of performing embodied cognition tasks in the real-world setting. We adopted independent component analysis (ICA) and electroencephalogram (EEG) dipole source localization methods to study the participant's event-related potentials (ERPs), event-related spectral perturbation (ERSP), and power spectral density (PSD) during the target detection task using a wireless EEG system, which is more convenient for real-life use. Behavioral results showed that the response time in the congruent condition (582 ms) was shorter than those in the incongruent (666 ms) and nontarget (863 ms) conditions. Regarding the EEG observation, we observed N200-P300 wave activation in the middle occipital lobe and P300-N500 wave activation in the right frontal lobe and left motor cortex, which are associated with attention ERPs. Furthermore, delta (1-4 Hz) and theta (4-7 Hz) band powers in the right frontal lobe, as well as alpha (8-12 Hz) and beta (13-30 Hz) band powers in the left motor cortex were suppressed, whereas the theta (4-7 Hz) band powers in the middle occipital lobe were increased considerably in the attention task. Experimental results showed that the embodied body function influences human mental states and psychological performance under cognition attention tasks. These neural markers will be also feasible to implement in the real-time brain computer interface. Novel findings in this study can be helpful for humans to further understand the interaction between the brain and behavior in multiple target detection conditions in real life.

Development of a Smart Helmet for Strategical BCI Applications.

Conducting electrophysiological measurements from human brain function provides a medium for sending commands and messages to the external world, as known as a brain-computer interface (BCI). In this study, we proposed a smart helmet which integrated the novel hygroscopic sponge electrodes and a combat helmet for BCI applications; with the smart helmet, soldiers can carry out extra tasks according to their intentions, i.e., through BCI techniques. There are several existing BCI methods which are distinct from each other; however, mutual issues exist regarding comfort and user acceptability when utilizing such BCI techniques in practical applications; one of the main challenges is the trade-off between using wet and dry electroencephalographic (EEG) electrodes. Recently, several dry EEG electrodes without the necessity of conductive gel have been developed for EEG data collection. Although the gel was claimed to be unnecessary, high contact impedance and low signal-to-noise ratio of dry EEG electrodes have turned out to be the main limitations. In this study, a smart helmet with novel hygroscopic sponge electrodes is developed and investigated for long-term usage of EEG data collection. The existing electrodes and EEG equipment regarding BCI applications were adopted to examine the proposed electrode. In the impedance test of a variety of electrodes, the sponge electrode showed performance averaging 118 kΩ, which was comparable with the best one among existing dry electrodes, which averaged 123 kΩ. The signals acquired from the sponge electrodes and the classic wet electrodes were analyzed with correlation analysis to study the effectiveness. The results indicated that the signals were similar to each other with an average correlation of 90.03% and 82.56% in two-second and ten-second temporal resolutions, respectively, and 97.18% in frequency responses. Furthermore, by applying the proposed differentiable power algorithm to the system, the average accuracy of 21 subjects can reach 91.11% in the steady-state visually evoked potential (SSVEP)-based BCI application regarding a simulated military mission. To sum up, the smart helmet is capable of assisting the soldiers to execute instructions with SSVEP-based BCI when their hands are not available and is a reliable piece of equipment for strategical applications.

Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine.

BACKGROUND: Brain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. However, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject's physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual's difference in SSVEP is needed but was seldom reported. METHODS: This paper presents a multiclass support vector machine (SVM)-based classification approach for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user's gaze targets. RESULTS: The on-line performance using the proposed approach has achieved high accuracy (89.88 ± 4.76%), fast responding time (effective epoch length = 1.13 ± 0.02 s), and the information transfer rate (ITR) was 50.91 ± 8.70 bits/min. CONCLUSIONS: The multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject's SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets.

An SSVEP-based BCI using high duty-cycle visual flicker.

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have generated significant interest due to their high information transfer rate (ITR). Due to the amplitude-frequency characteristic of the SSVEP, the flickering frequency of an SSVEP-based BCI is typically lower than 20 Hz to achieve a high SNR. However, a visual flicker with a flashing frequency below the critical flicker-fusion frequency often makes subjects feel flicker jerky and causes visual discomfort. This study presents a novel technique using high duty-cycle visual flicker to decrease user's visual discomfort. The proposed design uses LEDs flashing at 13.16 Hz, driven by flickering sequences consisting of repetitive stimulus cycles with a duration T (T = 76 ms). Each stimulus cycle included an ON state with a duration T(ON) and an OFF state with a duration T(OFF) (T = T(ON) + T(OFF)), and the duty cycle, defined as T(ON)/T, varied from 10.5% to 89.5%. This study also includes a questionnaire survey and analyzes the SSVEPs induced by different duty-cycle flickers. An 89.5% duty-cycle flicker, reported as a comfortable flicker, was adopted in a phase-tagged SSVEP system. Six subjects were asked to sequentially input a sequence of cursor commands with the 25.08-bits/min ITR.

Intermittent θ burst stimulation over primary motor cortex enhances movement-related ß synchronisation.

OBJECTIVE: The objective of this study is to investigate how transcranial magnetic intermittent theta burst stimulation (iTBS) with a prolonged protocol affects human cortical excitability and movement-related oscillations. METHODS: Using motor-evoked potentials (MEPs) and movement-related magnetoencephalography (MEG), we assessed the changes of corticospinal excitability and cortical oscillations after iTBS with double the conventional stimulation time (1200 pulses, iTBS1200) over the primary motor cortex (M1) in 10 healthy subjects. Continuous TBS (cTBS1200) and sham stimulation served as controls. RESULTS: iTBS1200 facilitated MEPs evoked from the conditioned M1, while inhibiting MEPs from the contralateral M1 for 30 min. By contrast, cTBS1200 inhibited MEPs from the conditioned M1. Importantly, empirical mode decomposition-based MEG analysis showed that the amplitude of post-movement beta synchronisation (16-26 Hz) was significantly increased by iTBS1200 at the conditioned M1, but was suppressed at the nonconditioned M1. Alpha (8-13 Hz) and low gamma-ranged (35-45 Hz) rhythms were not notably affected. Movement kinetics remained consistent throughout. CONCLUSIONS: TBS1200 modulated corticospinal excitability in parallel with the direction of conventional paradigms with modestly prolonged efficacy. Moreover, iTBS1200 increased post-movement beta synchronisation of the stimulated M1, and decreased that of the contralateral M1, probably through interhemispheric interaction. SIGNIFICANCE: Our results provide insight into the underlying mechanism of TBS and reinforce the connection between movement-related beta synchronisation and corticospinal output.

Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition.

BACKGROUND: Brain oscillatory activities are stochastic and non-linearly dynamic, due to their non-phase-locked nature and inter-trial variability. Non-phase-locked rhythmic signals can vary from trial-to-trial dependent upon variations in a subject's performance and state, which may be linked to fluctuations in expectation, attention, arousal, and task strategy. Therefore, a method that permits the extraction of the oscillatory signal on a single-trial basis is important for the study of subtle brain dynamics, which can be used as probes to study neurophysiology in normal brain and pathophysiology in the diseased. METHODS: This paper presents an empirical mode decomposition (EMD)-based spatiotemporal approach to extract neural oscillatory activities from multi-channel electroencephalograph (EEG) data. The efficacy of this approach manifests in extracting single-trial post-movement beta activities when performing a right index-finger lifting task. In each single trial, an EEG epoch recorded at the channel of interest (CI) was first separated into a number of intrinsic mode functions (IMFs). Sensorimotor-related oscillatory activities were reconstructed from sensorimotor-related IMFs chosen by a spatial map matching process. Post-movement beta activities were acquired by band-pass filtering the sensorimotor-related oscillatory activities within a trial-specific beta band. Signal envelopes of post-movement beta activities were detected using amplitude modulation (AM) method to obtain post-movement beta event-related synchronization (PM-bERS). The maximum amplitude in the PM-bERS within the post-movement period was subtracted by the mean amplitude of the reference period to find the single-trial beta rebound (BR). RESULTS: The results showed single-trial BRs computed by the current method were significantly higher than those obtained from conventional average method (P < 0.01; matched-pair Wilcoxon test). The proposed method provides high signal-to-noise ratio (SNR) through an EMD-based decomposition and reconstruction process, which enables event-related oscillatory activities to be examined on a single-trial basis. CONCLUSIONS: The EMD-based method is effective for artefact removal and extracting reliable neural features of non-phase-locked oscillatory activities in multi-channel EEG data. The high extraction rate of the proposed method enables the trial-by-trial variability of oscillatory activities can be examined, which provide a possibility for future profound study of subtle brain dynamics.