Wearable technologies for smart environments: A review with emphasis on BCI

Udovičić, Goran; Topic, Ante; Russo, Mladen

doi:10.1109/softcom.2016.7772186

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Due to its relatively low cost, ease of use, and sufficiently high temporal resolution, EEG-based BCI development has seen a wide range of use in medical applications [3,4], including neurorehabilitation [5], disorder diagnosis [6], and entertainment applications such as smart environments [7] and biofeedback-based games [8].…”

Section: Introductionmentioning

confidence: 99%

Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification

Roots

Muhammad

2020

Computers

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Brain–computer interfaces (BCIs) can help people with limited motor abilities to interact with their environment without external assistance. A major challenge in electroencephalogram (EEG)-based BCI development and research is the cross-subject classification of motor imagery data. Due to the highly individualized nature of EEG signals, it has been difficult to develop a cross-subject classification method that achieves sufficiently high accuracy when predicting the subject’s intention. In this study, we propose a multi-branch 2D convolutional neural network (CNN) that utilizes different hyperparameter values for each branch and is more flexible to data from different subjects. Our model, EEGNet Fusion, achieves 84.1% and 83.8% accuracy when tested on the 103-subject eegmmidb dataset for executed and imagined motor actions, respectively. The model achieved statistically significantly higher results compared with three state-of-the-art CNN classifiers: EEGNet, ShallowConvNet, and DeepConvNet. However, the computational cost of the proposed model is up to four times higher than the model with the lowest computational cost used for comparison.

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Section: Introductionmentioning

confidence: 99%

Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification

Roots

Muhammad

2020

Computers

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“…Moving to the technological aspects, a brief review on wearable technologies for smart environments is presented in [76] where the authors dedicate two sections on devices and applications for BCI systems. Considering these last topics, the technologies available at the time of the review (2016) are precisely listed and advancements of the EEG devices are easily detectable, especially considering the use of dry sensors and the presence of products for the general public.…”

Section: Overview Of Survey Articles On Eeg-based Bcismentioning

confidence: 99%

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

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In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

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“…One of the areas in which wearable sensors have been applied is in the study of human psychophysiology using Brain–Computer Interfaces (BCI) [ 4 ]. BCI systems consist on the automatic communication between the brain and the computer, usually using specific sensing devices [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Varandas

Lima

Badia

et al. 2022

Sensors

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Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91±13.67%. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.

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Wearable technologies for smart environments: A review with emphasis on BCI

Cited by 14 publications

References 38 publications

Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification

Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

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