Heuristic BCI System Recognizing the Cognitive Situation from Various EEG Patterns Induced by the Same Cognitive Task

Oda, Teruo; Kudoh, Nobuo

doi:10.1109/smc.2018.00651

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we developed a BCI system (Oda and Kudoh, 2017 , 2018 ) that extracts the motion intention more easily and quickly by simply patterning the increase/decrease of the amplitude of the EEG power using a template matching method (FTM) based on fuzzy reasoning, without using a technique to detect known EEG feature patterns in specific measurement sites and frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Elucidation of EEG Characteristics of Fuzzy Reasoning-Based Heuristic BCI and Its Application to Patient With Brain Infarction

et al. 2021

Self Cite

View full text Add to dashboard Cite

Non-invasive brain–computer interfaces (BCIs) based on common electroencephalography (EEG) are limited to specific instrumentation sites and frequency bands. These BCI induce certain targeted electroencephalographic features of cognitive tasks, identify them, and determine BCI's performance, and use machine-learning to extract these electroencephalographic features, which makes them enormously time-consuming. In addition, there is a problem in which the neurorehabilitation using BCI cannot receive ambulatory and immediate rehabilitation training. Therefore, we proposed an exploratory BCI that did not limit the targeted electroencephalographic features. This system did not determine the electroencephalographic features in advance, determined the frequency bands and measurement sites appropriate for detecting electroencephalographic features based on their target movements, measured the electroencephalogram, created each rule (template) with only large “High” or small “Low” electroencephalograms for arbitrarily determined thresholds (classification of cognitive tasks in the imaginary state of moving the feet by the size of the area constituted by the power spectrum of the EEG in each frequency band), and successfully detected the movement intention by detecting the electroencephalogram consistent with the rules during motor tasks using a fuzzy inference-based template matching method (FTM). However, the electroencephalographic features acquired by this BCI are not known, and their usefulness for patients with actual cerebral infarction is not known. Therefore, this study clarifies the electroencephalographic features captured by the heuristic BCI, as well as clarifies the effectiveness and challenges of this system by its application to patients with cerebral infarction.

show abstract

Section: Introductionmentioning

confidence: 99%