Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface

Han

et al. 2018

Sensors

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

Section: Introductionmentioning

confidence: 99%

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Han

et al. 2018

Sensors

“…Although serial combination can achieve satisfactory classification results, the contribution of different features to the classification is ignored. In [29], a Bayesian fusion approach based on the weighted average method was proposed. The core of this method is that the contribution of each modality can be automatically weighed to optimize performance, and it will give access to the metric that best classifies the data.…”

Section: Feature Fusionmentioning

confidence: 99%

“…The core of this method is that the contribution of each modality can be automatically weighed to optimize performance, and it will give access to the metric that best classifies the data. This method is similar to hybrid-BCI systems and more details can be found in [29][30][31]. Therefore, the weight can be computed as follows:…”

Section: Feature Fusionmentioning

confidence: 99%

“…Fast independent component analysis was used for denoising to obtain more pure EEG signals, ensuring the effectiveness of the extracted features in the subsequent work. The ERD/ERS phenomenon is related to the mu (8-12 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) rhythms of EEG signals. However, some frequencies in the beta rhythm are the harmonic waves of the mu rhythm.…”

Section: Eeg Data Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Luo

2020

Electronics

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification.

“…Some works use physiological sensors to infer internal states of the human subject, such as stress level, 54 however EEG provides much more direct evidence. 55,56 Wireless EEG devices allow much more natural experiences such as joint recording of body motion and the EEG of piano player while performing a simple tune. 57 We postulate that the experimental works reported in this paper fall near the domain of human computational neuroethology.…”

Section: Introductionmentioning

confidence: 99%

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

Graña

Aguilar-Moreno

Asiaín

et al. 2020

Int. J. Neur. Syst.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.