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.

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.

BackgroundBrain 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.MethodsThis 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).ResultsThe 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.ConclusionsThe 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.

Unacceptable moire distortion may result when images that include periodic structures such as halftone dots are scanned. In the frequency domain, moire patterns correspond to visible aliased frequencies. In the spatial domain, moire patterns are evident as cyclic changes in the size of halftone dots, producing visible periodic "beat" patterns.Moire pattern formation depends on the following factors: (1) the halftone screen frequency, (2) the scan frequency, (3) the angle between the scan direction and the halftone screen, (4) the scanner aperture size and shape, (5) quantization errors from the thresholding operation, (6) scanner and printer noise, and (7) the ink flow in the paper during printing. This paper analyzes the visibility of moire patterns in terms of these factors. In addition, the paper describes an approach to reducing the visibility of moire patterns by directly manipulating the moire formation factors. With an appropriate selection of the scan frequency and screen angle for a given screened image, moire beat frequencies in the scanned -Optical Instrumentation Engineers.2). The formation of moire patterns depends on the following factors: (1) the halftone screen frequency, (2) the scan frequency, (3) the angle between the scan direction and the halftone screen, (4) the scanner aperture size and shape, (5) quantization errors from the thresholding operation, (6) scanner and printer noise, and (7) the ink flow on the paper during printing. In the spatial domain, moire patterns can be described as visible "beat" patterns resulting from the incorrect reproduction of halftone dots. As shown in Fig. 3, halftone dots 1 and 2 are the same size before digitization. After digitization, halftone dot 1 consists of one black pixel and halftone dot 2 consists of two black pixels. A band of two -pixel dots appears darker than a band of one -pixel dots, producing "beat" patterns. In the frequency domain, moire patterns are seen as aliased frequency components resulting from the scanning of screened art that possesses infinite bandwidth. Figure 4(a) illustrates the relation between reflectance intensity and position for one horizontal scan line in Fig. 1. Figure 4(b) illustrates the frequency components of Fig. 4(a) in the Fourier domain. Figure 4(c) shows the aliased frequency components after scanning. These aliased frequency components correspond to visible moire patterns in the scanned image.Much research has been done on the analysis and reduction of moire patterns by postscan processing.'-' Rosenfeld and Kak showed that moire patterns are caused by aliased frequencies produced when sampling images containing OPTICAL ENGINEERING / July 1989 / Vol. 28 No. 7 / 805Abstract. Unacceptable moire distortion may result when images that include periodic structures such as halftone dots are scanned. In the frequency domain, moire patterns correspond to visible aliased frequencies. In the spatial domain, moire patterns are evident as cyclic changes in the size of halftone dots, producing visible periodic "beat" patterns....

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