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Abstract
- Two experimental paradigms :
- EEG-based system that is able to detect high mental workload
in drivers operating under real traffic condition
① An auditory workload scheme
② A mental calculation task
- performance is strongly subject-dependent;
however, the results are good to excellent for the majority of subjects
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Introduction
- Important issue in fields : pilots, flight controllers,
operators of industrial plants
- Be integrated with existing systems to maximize the performance
- Approach #1 : A closed-loop system
The system’s interaction with the operator is adjusted
According to the operator’s mental workload
- Approach #2 : The workload detector as an objective measure of mental workload
To develop improved modes of human-machine interaction
- Driving a car while performing additional tasks that model interaction
with the car’s systems To obtain a system that is able to measure and mitigate
mental workload (1) in real time and (2) in a real operational environment
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The Experimental Setup
- A system that is able to measure and mitigate mental workload
in real time and in real operational environments.
⊙ the execution of secondary tasks not related to driving
⊙ Twelve male and five female subjects age 20 to 32 years old
⊙ Approximately 100 km/h on the highway in moderate traffic conditions
⊙ Not to speak during the experiment in order to avoid additional workload
- Perform three types of tasks : #1 Driving the vehicle
#2 An auditory reaction time task
#3 A High mental workload task
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#3 - 2 An auditory reaction time task
#3 - 1 A mental calculation task
① German words links (left) and rechts (right) were randomly presented every 7.5 s
② Asked to count down in steps of twenty-seven
③ After two minutes, the subjects were asked for the final result
① A female news reader and a male voice reciting from a book
② The subject were instructed to follow the latter. ( To verify whether the subjects were engaged or not, they had to answer related
questions)
Subject had to their attention to one of two simultaneously presented voice recordings,
replicating a situation in which several vehicle occupants are talking at the same time.
#2 An auditory reaction time task
① Give three digit random number (between 800 and 999).
② Press corresponding buttons
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A crucial purpose of the experiment is to investigate
whether the output of the workload detector can be used
to control the secondary task
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Online Detection of Mental Workload
#1 The Workload detector
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⊙ Artifact channel removal
Such broad-band differences are characteristic for muscle artifacts (> 20 Hz) or eye artifacts (< 6 Hz).
The channels that exhibit those differences are excluded.
⊙ Selection subset channelSubset is one of four candidate sets that potentially include frontal, occipital, and temporal scalp positions.
⊙ Spatial filteringEach of the selected channels is normalized by the common median reference signal
(the median of all channels is subtracted from each channel)
⊙ Bandpass filteringbandpass filter using one of the bands listed above
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⊙ ClassificationLinear model whose parameters are computed by standard
linear discriminant analysis (LDA) of the feature vectors
The output is scalar value : Low workload (values below zero)
High workload (values above zero)
high and low workload, by means of a threshold scheme that employs
a hysteresis, which makes the classification substantially more robust
(ml < mh)
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⊙ Parameter Calibration
To find suitable values for all the previously mentioned subject- and task-specific parameters,
they use the well known cross-validation Technique.
This procedure is performed for each possible combination of parameter candidates in the feature extraction part
(EEG channel subset, spatial filter, frequency band, window length)
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Result
#1 Neurophysiological Interpretation
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#2 Accuracy of the Workload Detector
Auditory workload : (95.6% correct) Mental calculation workload : (91.8% correct)
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#3 Performance Improvement
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AbstractMethodology for relating neural variability to response variability, describing studies for response accuracy and response latency during visual target detection.
Introduction
- Analysis of trial-averaged ERP in EEG has enabled one to assess the speed of visual recognition
and discrimination in terms of the timing of the underlying neural processes.
- More recent work has used single-trial analysis of EEG to characterize the neural activity directly
correlated with behavioral variability during tasks involving rapid visual discrimination.
- These results suggest that components extracted from the EEG can capture the neural correlates
of the visual recognition and decision making processing on a trial-by-trial basis.
‘ Cortically-coupled computer vision’
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Linear methods for single-trial analysis
- The goal of a BCI system is to detect neuronal activity associated with cognitive events.
- Identify only one type of event (visual target recognition, differentiate this from other visual processing)
- 64channels , 1000Hz, time window is 0.5s (32000 samples, 32000 dimensional feature vector)
Sampling1000Hz (up to 500Hz) => L=50 time window, 20Hz (up to10Hz), 640 dimension
- The task is a binary classification
- The EEG activity is recorded as D x T values : D is the number of channels
T is the number of samples
- For reasonable classification :
(1) Reduce the trial-to-trial variability by filtering the signal to remove 60Hz interference and slow drifts.
(2) Reduce the dimentionality of the problem by stepping classification window every L-th sample.
L = 50, the signal of interest is at 10Hz while faster signal variation are considered noise.
1 , 2, 3. . . . . . . .500
1
1
.
.
1 . . . . 498,499,500
D
T
1 , 2, 3. . . . . . . . .10
1
1
.
.
1 . . . . . . . . . . 8,9,10
D
T
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X is projected onto a single dimension y.
(X is a matrix of channels by samples, and
y is a row vector containing multiple samples).
In this equation the inner product computes
the average over trials and samples.
When the intensity y averaged within
the specified time window is used as classification
criteria we achieve on this data an Az-value of 0.84.
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EEG correlates of perceptual decision making - Using single-trial linear discrimination analysis
To identify the cortical correlates of decision making during rapid discrimination of images
- A series of target (faces) and non-target (cars)
trials are presented in rapid succession
- Stimulus evidence is varied by manipulating
the phase coherence of the images
- Using a set of 12 face (Max Planck Institute face database)
and 12 car grayscale images
(image size 512 x 512 pixels, 8-bits/pixel).
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Early component ( = 170 ms following stimulus)
Late component ( > 300 ms following stimulus)
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Identifying cortical processes leading to response time variability
- Studying visual target detection using an RSVP paradigm
- During this task, participants are presented with a continuous sequence of natural scenes.
RSVP : rapid serial visual presentation
- 4 blocks of 50 sequences
- Each sequence has a 50% chance of containing one target image with one or more people
in a natural scene
- These target images can only appear within the middle 30 images of each 50 image sequence .
- The Each image was presented for 100 ms
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- Gaussian profile :
- Linear regression coefficients :
latency of the component activity
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