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Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 33843

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Special Issue Editors

Prof. Dr. Fabio Babiloni

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Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: cognitive brain activity; industrial neuroscience; brain–computer interface
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wanzeng Kong

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College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: brain–computer interface; cognitive computing
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Dr. Gianluca Di Flumeri

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Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
Interests: cognitive neuroscience; behavioural neuroscience; neuropsychology, biosignals processing; brain-computer interface; human-machine interaction; human factor; road safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

BCIs and passive BCIs are related to the monitoring of the internal brain states of users/patients to give a feedback to them in real time or to feed some artificial intelligence systems behind the devices. Sensors is also oriented to host papers not only related to physical sensors (as for instance for the evaluation of different EEG electrodes) but also to the computational methodologies able to further expand the sensitivity of such EEG sensors. In such context, this Special Issue aims to collate submissions from the different worlds of clinical practitioners and academics interested in the brain responses of professional categories to stress or cognitive tasks (as, for instance driving a car, etc.).

We are particularly interested in articles describing innovative Brain–Computer Interface (BCI) paradigms and applications on patients as well as healthy people. In fact, in the last decade, the BCIs, in their passive declination, have been more and more employed with successful and intriguing resulst in applications such as online monitoring of cognitive and emotional state in complex cognitive tasks; analysis of consumers’ perception in marketing or advertising experiences; measuring brain activity during fruition of art works and architectonic environments; and several other contexts, paving the way for a future massive use of neuroimaging-based devices during normal daylife activities.

Possible topics include, but are not limited to:

Brain–Computer Interfaces, developments and application to patients for neurorehabilitation, domotic, serious gaming and entertainment;
Methods for the analysis of EEG or MEG data from patients in relevant clinical contexts (e.g., data from cochlear implanted patients, or from deep brain stimulations);
Methods for on-line analysis of EEG or MEG data from normal subjects or patients in challenging contexts (driving car, airplanes, monitoring, video-surveillance, etc.);
Methodologies for the analysis of real-time neuroelectromagnetic cerebral signals;
Methods for real-time and offline rejection of muscle, eyes, movement-induced artifacts on neuroelectromagnetic cerebral signals during BCI applications;
Characterization of the electrodes to be used during the entire daylife activities in healthy persons and patients;
Multi-user Brain–Computer Interface and its applications.

Prof. Dr. Fabio Babiloni
Prof. Dr. Wanzeng Kong
Dr. Gianluca Di Flumeri
Guest Editors

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Keywords

Brain–Computer Interfaces (BCIs)
Passive BCIs
EEG
Rehabilitation
Human Factor
Cognitive workload
Attention
Vigilance
Hyperscanning

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Published Papers (7 papers)

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Research

18 pages, 2022 KiB

Open AccessArticle

The Sample Size Matters: To What Extent the Participant Reduction Affects the Outcomes of a Neuroscientific Research. A Case-Study in Neuromarketing Field

by Alessia Vozzi, Vincenzo Ronca, Pietro Aricò, Gianluca Borghini, Nicolina Sciaraffa, Patrizia Cherubino, Arianna Trettel, Fabio Babiloni and Gianluca Di Flumeri

Sensors 2021, 21(18), 6088; https://doi.org/10.3390/s21186088 - 10 Sep 2021

Cited by 33 | Viewed by 5789

Abstract

The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration. Full article

(This article belongs to the Special Issue Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities)

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25 pages, 1805 KiB

Open AccessArticle

BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification

by Aurélien Appriou, Léa Pillette, David Trocellier, Dan Dutartre, Andrzej Cichocki and Fabien Lotte

Sensors 2021, 21(17), 5740; https://doi.org/10.3390/s21175740 - 26 Aug 2021

Cited by 7 | Viewed by 4044

Abstract

Research on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals. Full article

(This article belongs to the Special Issue Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities)

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18 pages, 3296 KiB

Open AccessArticle

Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI

by Kyungho Won, Moonyoung Kwon, Minkyu Ahn and Sung Chan Jun

Sensors 2021, 21(16), 5436; https://doi.org/10.3390/s21165436 - 12 Aug 2021

Cited by 5 | Viewed by 2586

Abstract

Brain–computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here. Full article

(This article belongs to the Special Issue Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities)

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26 pages, 1750 KiB

Open AccessArticle

Real-Time Excitation of Slow Oscillations during Deep Sleep Using Acoustic Stimulation

by Marek Piorecky, Vlastimil Koudelka, Vaclava Piorecka, Jan Strobl, Daniela Dudysova and Jana Koprivova

Sensors 2021, 21(15), 5169; https://doi.org/10.3390/s21155169 - 30 Jul 2021

Cited by 6 | Viewed by 3382

Abstract

Slow-wave synchronous acoustic stimulation is a promising research and therapeutic tool. It is essential to clearly understand the principles of the synchronization methods, to know their performances and limitations, and, most importantly, to have a clear picture of the effect of stimulation on slow-wave activity (SWA). This paper covers the mentioned and currently missing parts of knowledge that are essential for the appropriate development of the method itself and future applications. Artificially streamed real sleep EEG data were used to quantitatively compare the two currently used real-time methods: the phase-locking loop (PLL) and the fixed-step stimulus in our own implementation. The fixed-step stimulation method was concluded to be more reliable and practically applicable compared to the PLL method. The sleep experiment with chronic insomnia patients in our sleep laboratory was analyzed in order to precisely characterize the effect of sound stimulation during deep sleep. We found that there is a significant phase synchronization of delta waves, which were shown to be the most sensitive metric of the effect of acoustic stimulation compared to commonly used averaged signal and power analyses. This finding may change the understanding of the effect and function of the SWA stimulation described in the literature. Full article

(This article belongs to the Special Issue Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities)

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19 pages, 2866 KiB

Open AccessArticle

SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms

by Alessandra Anzolin, Jlenia Toppi, Manuela Petti, Febo Cincotti and Laura Astolfi

Sensors 2021, 21(11), 3632; https://doi.org/10.3390/s21113632 - 23 May 2021

Cited by 15 | Viewed by 5527

Abstract

EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user’s needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness. Full article

(This article belongs to the Special Issue Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities)

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20 pages, 978 KiB

Open AccessArticle

Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition

by Fangyao Shen, Yong Peng, Wanzeng Kong and Guojun Dai

Sensors 2021, 21(4), 1262; https://doi.org/10.3390/s21041262 - 10 Feb 2021

Cited by 22 | Viewed by 4717

Abstract

Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present a novel multi-scale frequency bands ensemble learning (MSFBEL) method to perform emotion recognition from EEG signals. Concretely, we first re-organize all frequency bands into several local scales and one global scale. Then we train a base classifier on each scale. Finally we fuse the results of all scales by designing an adaptive weight learning method which automatically assigns larger weights to more important scales to further improve the performance. The proposed method is validated on two public data sets. For the “SEED IV” data set, MSFBEL achieves average accuracies of 82.75%, 87.87%, and 78.27% on the three sessions under the within-session experimental paradigm. For the “DEAP” data set, it obtains average accuracy of 74.22% for four-category classification under 5-fold cross validation. The experimental results demonstrate that the scale of frequency bands influences the emotion recognition rate, while the global scale that directly concatenating all frequency bands cannot always guarantee to obtain the best emotion recognition performance. Different scales provide complementary information to each other, and the proposed adaptive weight learning method can effectively fuse them to further enhance the performance. Full article

(This article belongs to the Special Issue Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities)

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17 pages, 2623 KiB

Open AccessArticle

An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

by Fangkun Zhu, Lu Jiang, Guoya Dong, Xiaorong Gao and Yijun Wang

Sensors 2021, 21(4), 1256; https://doi.org/10.3390/s21041256 - 10 Feb 2021

Cited by 35 | Viewed by 5780

Abstract

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs. Full article

(This article belongs to the Special Issue Brain–Computer Interfaces (BCI) and Application in Healthy and Daily Life Activities)

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