Alternative Classification Techniques for Brain-Computer Interfaces for Smart Sensor Manufacturing Environments

Balderas, David; Molina, Arturo; Ponce, Pedro

doi:10.1016/j.ifacol.2015.06.161

Cited by 10 publications

(8 citation statements)

References 12 publications

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“…Theoretically, the deployment of BCI applications in Industry 4.0 could contribute to put the operator back at the center of industrial processes. The possible industrial applications could be categorized as follows: (1) safety at work, (2) adaptive training and (3) device's control (e.g., Tamburrini, 2014;Balderas et al, 2015;Oztemel and Gursev, 2018).…”

Section: Resultsmentioning

confidence: 99%

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Douibi

Bars

Lemontey

et al. 2021

Front. Hum. Neurosci.

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In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

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Section: Resultsmentioning

confidence: 99%

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Douibi

Bars

Lemontey

et al. 2021

Front. Hum. Neurosci.

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“…Frequency domain characteristics are related to changes in oscillatory activity. The human EEG can be roughly categorized into 4 frequency bands [26,27]: delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), and beta (13-30 Hz) oscillations.…”

Section: Closingmentioning

confidence: 99%

YouTube Videos in the Virtual Flipped Classroom Model Using Brain Signals and Facial Expressions

2021

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As a result of the confinement due to the COVID-19 pandemic, various educational institutions migrated their face-to-face teaching modality to a virtual modality. This article presents the implementation of the Flipped Classroom model in a completely virtual format to develop grammatical competency in Spanish. The model used videos from YouTube, one of the leading global social network platforms, and the videoconferencing system Zoom, the tool selected by the studied educational institution to continue academic operations during the health confinement. The model was enriched with the Index for Learning Style test to provide more differentiated teaching. This study showed considerable improvement in the academic performance of high school students taking a Spanish course at the Mexico City campus of Tecnologico de Monterrey. Of the total sample, 98% increased their score by between 2 and 46 points, from a total of 100, in their grammatical competency in Spanish. Additionally, the student satisfaction survey showed that more than 90% considered the course methodology beneficial for developing their grammatical competency in Spanish. This study demonstrates the potential of the Flipped Classroom model in a virtual format. This teaching structure using the Flipped Classroom model could be replicated in various educational settings and for different areas of knowledge.

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“…class dataset was collected from 3 subjects including combinations of the following mental tasks: clenching of right hand, shaking of left leg, visualization of a tumbling cube, counting backward from 100 by 3's, and singing a favorite song. Balderas et al[148], 2015 EEG classification for 2 class motor imagery (left hand and right hand)An LSTM based classifier was trained and evaluated for EEG oscillatory components classification and compared with the regular neural network implementations.…”

mentioning

confidence: 99%

A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

2021

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Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.

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Alternative Classification Techniques for Brain-Computer Interfaces for Smart Sensor Manufacturing Environments

Cited by 10 publications

References 12 publications

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications

YouTube Videos in the Virtual Flipped Classroom Model Using Brain Signals and Facial Expressions

A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

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