Would this provide an objective measurement of stroke fatigue? Ask your doctor for how they are measuring your fatigue and what they are doing to solve your fatigue.
Detecting passive fatigue with brain-computer interface in drowsy driving and stroke rehabilitation
Date of Issue
2019School
School of Computer Science and Engineering
Research Centre
Centre for Computational Intelligence
Related Organization
Institute for Infocomm Research, A*STAR
Abstract
A recent topic of interest for electroencephalography(EEG)-based
brain-computer interface (BCI) is mental state monitoring. Mental states
such as fatigue, frustration and attention have been shown to affect
healthy users’ BCI performance. However, ground truth of mental states
is often difficult to ascertain. Instead, surrogate measures such as
ratings or reaction times have been used. Additionally, mental state
monitoring has yet to be applied in the medical domain. Currently, a
handful of studies have reported the mental state of fatigue in motor
imagery (MI)-BCI stroke rehabilitation only via subject ratings, but not
EEG data. Hence, there is a need to develop algorithms that can detect
mental states despite a lack of ground truth. In particular, this thesis
focuses on detecting the mental state of passive fatigue induced by
monotony in healthy subjects in drowsy driving and in stroke survivors
using MI-BCI stroke rehabilitation.
A two-step iterative cross-subject negative-unlabeled (NU) learning
algorithm is proposed to address the lack of ground truth of passive
fatigue in drowsy driving studies. In the first step, subjects’ alert
and unlabeled driving EEG data are iteratively used to label each
subject’s most fatigued driving block. The second step then used the
alert and newly-labeled fatigued blocks to compute subjects’ fatigue
score.
A drowsy driving experiment is conducted to verify effectiveness of the
NU learning algorithm. Dry EEG MuseTM headband data is collected from 29
healthy subjects. The results showed that the algorithm converged in 7
iterations and yielded a high mean accuracy of 93.77% ± 8.15% in
detecting fatigue in a cross-subject manner. The results also showed
that the fatigue score is significantly negatively correlated with
relative EEG beta band power, an indicator of passive fatigue. This
suggested that high fatigue score is associated with low beta band power
and therefore low brain arousal. Hence the proposed algorithm was able
to quantify passive fatigue well.
In the medical domain, a clinical trial is conducted with Neurostyle
Brain Exercise Therapy Towards Enhanced Recovery (nBETTER), an EEG-based
MI-BCI with only visual feedback. Subjects’ Fugl-Meyer Motor Assessment
(FMA) score is measured to assess clinical efficacy. Also, BCI
performance is correlated with relative EEG beta band power and the
proposed NU learning algorithm is applied as well to investigate the
presence of fatigue. The results showed significant FMA score gains, but
are comparable to a retrospective control group. The results also
showed a significant positive correlation of BCI performance with beta
band power. Additionally, the proposed NU learning algorithm yielded
fatigue scores that are non-significantly negatively correlation with
BCI performance. Together, these suggested that fatigue might be
present, resulting in the poorer BCI performance and comparable efficacy
to the control group. This postulated a disadvantage of MI-BCI systems
in inducing fatigue due to the monotony of performing MI repetitively.
These studies have presented the potential of BCI to play a fatigue
monitoring role, by presenting a cross-subject algorithm to detect and
quantify fatigue from alert data in a drowsy driving setting, and
finding the possibility of fatigue during a MI-BCI intervention.
Subject
Engineering::Computer science and engineering::Computer applications
Type
Thesis
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