Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

A Kamm's Circle-Based Potential Risk Estimation Scheme in the Local Dynamic Map Computation Enhanced by Binary Decision Diagrams.

Autonomous vehicles (AV) are a hot topic for safe mobility, which inevitably requires sensors to achieve autonomy, but relying too heavily on sensors will be a risk factor. A high-definition map (HD map) reduces the risk by giving geographical information if it covers dynamic information from moving entities on the road. Cooperative intelligent transport systems (C-ITS) are a prominent approach to solving the issue and local dynamic maps (LDMs) are expected to realize the ideal C-ITS. An actual LDM implementation requires a fine database design to be able to update the information to represent potential risks based on future interactions of vehicles. In the present study, we proposed an advanced method for embedding the geographical future occupancy of vehicles into the database by using a binary decision diagram (BDD). In our method, the geographical future occupancy of vehicles was formulated with Kamm's circle. In computer experiments, sharing BDD-based occupancy data was successfully demonstrated in the ROS-based simulator with the linked list-based BDD. Algebraic operations in exchanged BDDs effectively managed future interactions such as data insertion and timing of collision avoidance in the LDM. This result opened a new door for the realization of the ideal LDM for safety in AVs.

Artificial Neural Network Approach to Guarantee the Positioning Accuracy of Moving Robots by Using the Integration of IMU/UWB with Motion Capture System Data Fusion.

This study presents an effective artificial neural network (ANN) approach to combine measurements from inertial measurement units (IMUs) and time-of-flight (TOF) measurements from an ultra-wideband (UWB) system with OptiTrack Motion Capture System (OptiT-MCS) data to guarantee the positioning accuracy of motion tracking in indoor environments. The proposed fusion approach unifies the following advantages of both technologies: high data rates from the MCS, and global translational precision from the inertial measurement unit (IMU)/UWB localization system. Consequently, it leads to accurate position estimates when compared with data from the IMU/UWB system relative to the OptiT-MCS reference system. The calibrations of the positioning IMU/UWB and MCS systems are utilized in real-time movement with a diverse set of motion recordings using a mobile robot. The proposed neural network (NN) approach experimentally revealed accurate position estimates, giving an enhancement average mean absolute percentage error (MAPE) of 17.56% and 7.48% in the X and Y coordinates, respectively, and the coefficient of correlation R greater than 99%. Moreover, the experimental results prove that the proposed NN fusion is capable of maintaining high accuracy in position estimates while preventing drift errors from increasing in an unbounded manner, implying that the proposed approach is more effective than the compared approaches.

Corrigendum: A Review of Possible EEG Markers of Abstraction, Attentiveness and Memorisation in Cyber-Physical Systems for Special Education.

[This corrects the article DOI: 10.3389/frobt.2021.715962.].

A Review of Possible EEG Markers of Abstraction, Attentiveness, and Memorisation in Cyber-Physical Systems for Special Education.

Cyber-physical systems (CPSs) for special education rely on effective mental and brain processing during the lesson, performed with the assistance of humanoid robots. The improved diagnostic ability of the CPS is a prerogative of the system for efficient technological support of the pedagogical process. The article focuses on the available knowledge of possible EEG markers of abstraction, attentiveness, and memorisation (in some cases combined with eye tracking) related to predicting effective mental and brain processing during the lesson. The role of processing abstraction is emphasised as the learning mechanism, which is given priority over the other mechanisms by the cognitive system. The main markers in focus are P1, N170, Novelty P3, RewP, N400, and P600. The description of the effects is accompanied by the analysis of some implications for the design of novel educational scenarios in inclusive classes.

MCA Based Epilepsy EEG Classification Using Time Frequency Domain Features.

In this work, we proposed a morphological component analysis (MCA) based method for epilepsy classification using the explicit dictionary of independent redundant transforms to decomposes the electroencephalogram (EEG) by considering it's morphology. Output components of MCA are represented into analytical form by using Hilbert transform. Then features, parameter's ratio of bandwidth square, mean square frequency and fractional contributions to dominant frequency were extracted to discriminate epilepsy EEG by support vector machine (SVM). These features have shown classification results comparable to previous works.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis.

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of "dictionary." MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

Direction and viewing area-sensitive influence of EOG artifacts revealed in the EEG topographic pattern analysis.

The influence of eye movement-related artifacts on electroencephalography (EEG) signals of human subjects, who were requested to perform a direction or viewing area dependent saccade task, was investigated by using a simultaneous recording with ocular potentials as electro-oculography (EOG). In the past, EOG artifact removals have been studied in tasks with a single fixation point in the screen center, with less attention to the sensitivity of cornea-retinal dipole orientations to the EEG head map. In the present study, we hypothesized the existence of a systematic EOG influence that differs according to coupling conditions of eye-movement directions with viewing areas including different fixation points. The effect was validated in the linear regression analysis by using 12 task conditions combining horizontal/vertical eye-movement direction and three segregated zones of gaze in the screen. In the first place, event-related potential topographic patterns were analyzed to compare the 12 conditions and propagation coefficients of the linear regression analysis were successively calculated in each condition. As a result, the EOG influences were significantly different in a large number of EEG channels, especially in the case of horizontal eye-movements. In the cross validation, the linear regression analysis using the appropriate dataset of the target direction/viewing area combination demonstrated an improved performance compared with the traditional methods using a single fixation at the center. This result may open a potential way to improve artifact correction methods by considering the systematic EOG influence that can be predicted according to the view angle such as using eye-tracker systems.

A unified view of theta-phase coding in the entorhinal-hippocampal system.

The discovery of theta-rhythm-dependent firing of rodent hippocampal neurons highlighted the functional significance of temporal encoding in hippocampal memory. However, earlier theoretical studies on this topic seem divergent and experimental implications are invariably complicated. To obtain a unified understanding of neural dynamics in the hippocampal memory, we here review recent developments in computational models and experimental discoveries on the 'theta-phase precession' of hippocampal place cells and entorhinal grid cells. We identify a theoretical hypothesis that is well supported by experimental facts; this model reveals a significant contribution of theta-phase coding to the on-line real-time operation of episodic events, through highly parallel representation of spatiotemporal information.

Neural dynamics of the cognitive map in the hippocampus.

The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. In the classical theory, the cognitive map has been explained as a consequence of the fact that different spatial regions are assigned to different cell populations in the framework of rate coding. Recently, the relation between place cell firing and local field oscillation theta in terms of theta phase precession was experimentally discovered and suggested as a temporal coding mechanism leading to memory formation of behavioral sequences accompanied with asymmetric Hebbian plasticity. The cognitive map theory is apparently outside of the sequence memory view. Therefore, theoretical analysis is necessary to consider the biological neural dynamics for the sequence encoding of the memory of behavioral sequences, providing the cognitive map formation. In this article, we summarize the theoretical neural dynamics of the real-time sequence encoding by theta phase precession, called theta phase coding, and review a series of theoretical models with the theta phase coding that we previously reported. With respect to memory encoding functions, instantaneous memory formation of one-time experience was first demonstrated, and then the ability of integration of memories of behavioral sequences into a network of the cognitive map was shown. In terms of memory retrieval functions, theta phase coding enables the hippocampus to represent the spatial location in the current behavioral context even with ambiguous sensory input when multiple sequences were coded. Finally, for utilization, retrieved temporal sequences in the hippocampus can be available for action selection, through the process of reverting theta rhythm-dependent activities to information in the behavioral time scale. This theoretical approach allows us to investigate how the behavioral sequences are encoded, updated, retrieved and used in the hippocampus, as the real-time interaction with the external environment. It may indeed be the bridge to the episodic memory function in human hippocampus.

Cognitive map formation through sequence encoding by theta phase precession.

The rodent hippocampus has been thought to represent the spatial environment as a cognitive map. The associative connections in the hippocampus imply that a neural entity represents the map as a geometrical network of hippocampal cells in terms of a chart. According to recent experimental observations, the cells fire successively relative to the theta oscillation of the local field potential, called theta phase precession, when the animal is running. This observation suggests the learning of temporal sequences with asymmetric connections in the hippocampus, but it also gives rather inconsistent implications on the formation of the chart that should consist of symmetric connections for space coding. In this study, we hypothesize that the chart is generated with theta phase coding through the integration of asymmetric connections. Our computer experiments use a hippocampal network model to demonstrate that a geometrical network is formed through running experiences in a few minutes. Asymmetric connections are found to remain and distribute heterogeneously in the network. The obtained network exhibits the spatial localization of activities at each instance as the chart does and their propagation that represents behavioral motions with multidirectional properties. We conclude that theta phase precession and the Hebbian rule with a time delay can provide the neural principles for learning the cognitive map.

Synchronization of neural oscillations as a possible mechanism underlying episodic memory: a study of theta rhythm in the hippocampus.

Rat hippocampal cells exhibit characteristic phase-dependent firings when oscillation in frequency range around 8Hz is present. Based on the hypothesis that theta phase coding is generated by synchronization of neural activities, an autoassociative network model of the hippocampus and the entorhinal cortex was analyzed to explore mechanisms underlying episodic memory. Phase coding in theta rhythm enables instantaneous acquisition of experienced events including temporal and spatial contents. Further comparison with electrophysiological data from both rodents and primates suggests a possible role of theta oscillations in memory encoding and online processing of episodic events.