Abstract
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.
Highlights
Brain–computer interfaces (BCIs), which are interfaces for detecting cerebral activity and controlling devices, are mainly used for EEG
Figure 2B(a) shows the output values obtained with fuzzy inference-based template matching method (FTM) in Task 2. It can be confirmed from the figure that the output value was below the threshold in the case patient as well as the healthy subjects at rest, and the output value was above the threshold in the motor imagery
In the neurorehabilitation system using the proposed Heuristic BCI with fuzzy reasoning, it was confirmed that motor imagery dorsiflexing the right foot and resting-state can be distinguished with high accuracy in healthy subjects and in the case patient, and that it is a rehabilitation system that can be used in motor imagery training
Summary
Brain–computer interfaces (BCIs), which are interfaces for detecting cerebral activity and controlling devices, are mainly used for EEG (electroencephalogram). Examples of applications of the BCIs include, for example, those that provide visual stimuli that call up event-related potential brain activity (ERPs) (Liu et al, 2000; Mattout et al, 2015) or stable-state visual evoked potentials (SSVEP) (Andrew et al, 2013; Norcia et al, 2015), but are difficult to include motion intentions or motor images that have such factors as the orientation and velocity of body movement Another example is BCIs that use frequency information (event-related synchronization/desynchronization, ERS/ERD) (Pfurtscheller et al, 2000; Wolpaw J. et al, 2000; Kudoh et al, 2008; Joen et al, 2011) that uses EEG synchronous and asynchronous components associated with an event. There are individual differences, ERD/ERS tend to be difficult to develop if there is no prior training, and many have been experimented with after several months or more of training (Wolpawand and Wolpaw, 2012; Shanechi et al, 2016)
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