Implementation of a Classification System of EEG Signals Based on FPGA

Asanza, Víctor; Constantine, Alisson; Valarezo, Stephany; Peláez, Enrique

doi:10.1109/icedeg48599.2020.9096752

Cited by 8 publications

(14 citation statements)

References 19 publications

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“…Figure 6 shows the devices used by the authors for recording EEG signals. There is commercial equipment such as: ETG-4000 [60], Neuroscan [62,63,70,76], BrainNet BNT 36 [68,69], Biotop 6 R-12 [73], BrainVision actiCHamp [74], ActiCap and two BrainAmp amplifiers (Brain Products GmbH) [75], NVX 52 amplifier [71], NuAmps [72], BrainBoard using an ADS-1299 chip module (an open-source EEG hardware platform) [77], NeXus-32 bioamplifiers [78], and BCI2000 [79]. Other authors have used combined equipment such as Two 32-channel EEG amplifiers (Synamps, Neuroscan) [65].…”

Section: Experimental Methodologymentioning

confidence: 99%

“…Finally, other authors have used the Active Two amplifier [59,66]. [60], Neuroscan [62,63,65,70,76,80], BrainNet BNT 36 [68,69], Biotop 6 R-12 [73], BrainVision actiCHamp [74], ActiCap and two BrainAmp amplifiers [75], NVX 52 amplifier [71], NuAmps [72], BrainBoard [77], NeXus-32 [78], BCI2000 [79], and Active Two amplifier [59,66]. The images displayed here are freely available from the manufacturer/provider's official website and are exclusively used for non-profit, academic purposes.…”

Section: Experimental Methodologymentioning

confidence: 99%

“…Auditory stimulation was used to indicate the execution of the task while the participant was thinking about the action. -Asanza et al [79] used a database of 64-channel EEG signals recorded using the so-called BCI2000 system. Both the acquisition system and the data are widely described in [81].…”

Section: Experimental Methodologymentioning

confidence: 99%

“…Electrooculography (EOG) sensors were also used. [59,67,[72][73][74], motor imagery [61,63,64,70,75,78], motor imagery-active movements [71,76,77,79], motor imagery-active movements-attempted movements [80], movement intention-active movements [60,66,68,69], assisted movements [62], and electrical lower limb stimulation [65]. [77] used non-stimulus BCI signal event-related desynchronization EEG (ERD).…”

Section: Experimental Methodologymentioning

confidence: 99%

“…The following is a summary of the classification algorithms used: [59,78], SVM [63,70,74,80], random forest [66], LDA [60,68,69,71,77], and NN [76,79].…”

Section: Classification Algorithmsmentioning

confidence: 99%

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Identification of Lower-Limb Motor Tasks via Brain–Computer Interfaces: A Topical Overview

Asanza

Peláez

Loayza

et al. 2022

Sensors

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Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

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Section: Experimental Methodologymentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

Section: Experimental Methodologymentioning

confidence: 99%

“…The following is a summary of the classification algorithms used: [59,78], SVM [63,70,74,80], random forest [66], LDA [60,68,69,71,77], and NN [76,79].…”

Section: Classification Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations