Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi, Kais; Gannouni, Sofien; Aboalsamh, Hatim

doi:10.3390/s21134293

Cited by 24 publications

(20 citation statements)

References 80 publications

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“…These results also demonstrate that low-density wearable EEG devices based on platforms such as BioWolf [10,12], typically used in a BCI context with high-stimulation frequencies (8-20 Hz) [16,38,39], can also provide reliable signal quality even with very low-stimulation frequencies (1 Hz). This denotes an excellent prospect for using these devices in research and clinical studies on various neurocognitive functions in out-of-the-lab environments and/or with special populations, such as newborns, infants, or patients.…”

Section: Discussionsupporting

confidence: 53%

Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis

Kartsch

Kumaravel

Benatti

et al. 2022

Sensors

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Recent studies show that the integrity of core perceptual and cognitive functions may be tested in a short time with Steady-State Visual Evoked Potentials (SSVEP) with low stimulation frequencies, between 1 and 10 Hz. Wearable EEG systems provide unique opportunities to test these brain functions on diverse populations in out-of-the-lab conditions. However, they also pose significant challenges as the number of EEG channels is typically limited, and the recording conditions might induce high noise levels, particularly for low frequencies. Here we tested the performance of Normalized Canonical Correlation Analysis (NCCA), a frequency-normalized version of CCA, to quantify SSVEP from wearable EEG data with stimulation frequencies ranging from 1 to 10 Hz. We validated NCCA on data collected with an 8-channel wearable wireless EEG system based on BioWolf, a compact, ultra-light, ultra-low-power recording platform. The results show that NCCA correctly and rapidly detects SSVEP at the stimulation frequency within a few cycles of stimulation, even at the lowest frequency (4 s recordings are sufficient for a stimulation frequency of 1 Hz), outperforming a state-of-the-art normalized power spectral measure. Importantly, no preliminary artifact correction or channel selection was required. Potential applications of these results to research and clinical studies are discussed.

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Section: Discussionsupporting

confidence: 53%

Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis

Kartsch

Kumaravel

Benatti

et al. 2022

Sensors

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“…When tadpoles are created to have eyes on their tails, instead of their heads, they are still readily able to perform visual learning tasks ( Blackiston and Levin, 2013 ; Blackiston et al, 2017 ). Planaria can readily be made with two (or more) brains in the same body ( Morgan, 1904 ; Oviedo et al, 2010 ), and human patients are now routinely augmented with novel inputs [such as sensory substitution ( Bach-y-Rita et al, 1969 ; Bach-y-Rita, 1981 ; Danilov and Tyler, 2005 ; Ptito et al, 2005 )] or novel effectors, such as instrumentized interfaces allowing thought to control engineered devices such as wheelchairs in addition to the default muscle-driven peripherals of their own bodies ( Green and Kalaska, 2011 ; Chamola et al, 2020 ; Belwafi et al, 2021 ). The central phenomenon here is plasticity: minds are not tightly bound to one specific underlying architecture (as most of our software is today), but readily mold to changes of genomic defaults.…”

Section: Cognition: Changing the Subjectmentioning

confidence: 99%

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

Levin

2022

Front. Syst. Neurosci.

135

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Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME—Technological Approach to Mind Everywhere—a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.

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“…The obtained accuracies were very promising and tended to be 97% for some subjects, and in the least, the obtained accuracy was about 90%. The obtained accuracies exceeded the requirement of the navigation accuracy by 20% [ 16 ]. The promising accuracies obtained according to the online approach clearly showed the efficiency of the proposed method as the accuracy was maintained above 70% for all subjects.…”

Section: Discussionmentioning

confidence: 97%

“…The offline approach was widely used in the literature because it used the existing benchmarks available on the Internet, such as the datasets provided by the BCI competition or PhysioNet. The two approaches are different in their implementation, and it should be noted that the performance obtained according to the offline approach decreased significantly compared to the online [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer

et al. 2022

Self Cite

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There are many applications controlled by the brain signals to bridge the gap in the digital divide between the disabled and the non-disabled people. The deployment of novel assistive technologies using brain-computer interface (BCI) will go a long way toward achieving this lofty goal, especially after the successes demonstrated by these technologies in the daily life of people with severe disabilities. This paper contributes in this direction by proposing an integrated framework to control the operating system functionalities using Electroencephalography signals. Different signal processing algorithms were applied to remove artifacts, extract features, and classify trials. The proposed approach includes different classification algorithms dedicated to detecting the P300 responses efficiently. The predicted commands passed through a socket to the API system, permitting the control of the operating system functionalities. The proposed system outperformed those obtained by the winners of the BCI competition and reached an accuracy average of 94.5% according to the offline approach. The framework was evaluated according to the online process and achieved an excellent accuracy attaining 97% for some users but not less than 90% for others. The suggested framework enhances the information accessibility for people with severe disabilities and helps them perform their daily tasks efficiently. It permits the interaction between the user and personal computers through the brain signals without any muscular efforts.

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Embedded Brain Computer Interface: State-of-the-Art in Research

Cited by 24 publications

References 80 publications

Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis

Efficient Low-Frequency SSVEP Detection with Wearable EEG Using Normalized Canonical Correlation Analysis

Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds

A Brain Controlled Command-Line Interface to Enhance the Accessibility of Severe Motor Disabled People to Personnel Computer

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