Identification of multiple-tasks-induced-EEG by heuristic BCI with learning type fuzzy-template-matching method

Oda, Teruo; Kudoh, Suguru N.

doi:10.1109/ifsa-scis.2017.8023260

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Two-sided t-test for each task compared to rest showed a significant difference of more than 5% in all tasks. The motor imagery effect on the EEG was compared with the control condition in which the machine moves the leg autonomously with the heuristic BCI algorithm [15,16]. Each column in Figure 10 shows independent component analysis (ICA) [21,22], where the relative activation area is highlighted in red as compared with a less activated area in blue and an average one in green.…”

Section: Resultsmentioning

confidence: 99%

“…Each fuzzy template was constructed with two fuzzy labels of "high" and "low" in an antecedent clause of a fuzzy rule. As shown in Figure 2, the 2 16 rule is constructed from 16 inputs based on the number of EEG measurement channels (8 electrodes, 2 frequencies), and 2 inputs based on the number of fuzzy labels. For instance, a template can be composed of inputs having features of different types such as "EEG power of Oz measurement site in the α wave frequency band" and "amplitude of the surface electromyography (EMG) signal of the lower right limb."…”

Section: Fuzzy Template Matching (Ftm)mentioning

confidence: 99%

“…The new template set increases the accuracy of identification of the two states, as shown in Figure 3. Consequent values were set up by the learning process [15,16]. Using the steepest descent method, Z is approximated to the target value of the teacher signal T in Equation (6).…”

Section: Pruning Fuzzy Rulesmentioning

confidence: 99%

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Prototype of an Ankle Neurorehabilitation System with Heuristic BCI Using Simplified Fuzzy Reasoning

et al. 2019

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Neurorehabilitation using a brain–computer interface (BCI) requires machine learning, for which calculations take a long time, even days. However, the demands of actual rehabilitation are becoming increasingly rigorous, requiring that processes be completed within tens of minutes. Therefore, we developed a new effective rehabilitation system for treating patients such as those with stroke hemiplegia. The system can smoothly perform rehabilitation training on the day of admission to the hospital. We designed a heuristic BCI with simplified fuzzy reasoning, which can detect motor intention signals from an electroencephalogram (EEG) within several tens of minutes. The detected signal is sent to the newly developed ankle rehabilitation device (ARD), and the patient repeats the dorsiflexion motion by the ARD.

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Section: Resultsmentioning

confidence: 99%

Section: Fuzzy Template Matching (Ftm)mentioning

confidence: 99%

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Prototype of an Ankle Neurorehabilitation System with Heuristic BCI Using Simplified Fuzzy Reasoning

et al. 2019

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“…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

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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.

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Evaluating Emotions while Watching a Movie using the Learning Type Fuzzy-Template-Matching method

Wang,

Kudoh

2023

International Conference on Human-Agent Interaction

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Identification of multiple-tasks-induced-EEG by heuristic BCI with learning type fuzzy-template-matching method

Cited by 4 publications

References 6 publications

Prototype of an Ankle Neurorehabilitation System with Heuristic BCI Using Simplified Fuzzy Reasoning

Prototype of an Ankle Neurorehabilitation System with Heuristic BCI Using Simplified Fuzzy Reasoning

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

Evaluating Emotions while Watching a Movie using the Learning Type Fuzzy-Template-Matching method

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