An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings †
Abstract
:1. Introduction
2. Materials and Methods
2.1. Canonical Correlation Analysis
2.2. Constructing Spatial Filters for ERP Extraction
2.3. Model Functions for ERPs
2.4. A Spatio-Temporal Filter
2.5. Recognition of ERP Sequences
2.6. Experimental Data
2.7. Processing of Experimental Data
3. Results
3.1. Accuracy of Recognition
3.2. Spatio-Temporal Patterns
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
ADHD | Attention deficit hyperactivity disorder |
BCI | Brain–computer interface |
CCA | Canonical correlation analysis |
CSP | Common spatial pattern |
EEG | Electroencephalogram |
ERP | Event-related potential |
ICA | Independent component analysis |
ITR | Information transfer rate |
MEG | Magnetoencephalogram |
P300 | Positive deflection, peaking at 300 ms |
PCA | Principal component analysis |
SD | Standard deviation |
ANR | Signal-to-noise ratio |
SVM | Support vector machine |
Nomenclature
c | number of variables (channels) in |
d | number of variables (reference functions) in |
e | index for event sequence |
f | averaged Fisher z-transformed correlation coefficients |
i | general iteration index |
k | index for columns in (virtual channel/component) |
m | element in |
n | number of observations (sampling points) |
p | p-value |
q | number of selected components |
s | refers to standard event |
t | refers to target event |
w | element in |
x | refers to / element in |
y | refers to / element in |
column vector in | |
column vector in | |
column vector in | |
identity matrix of size | |
matrix of model functions | |
matrix of filtered | |
matrix of filtered | |
filter matrix (weights for linear combination) | |
matrix of observations (brain signals) | |
matrix of observations (reference signals) | |
ρ | (canonical) correlation coefficient |
μ | Gaussian mean |
σ | standard deviation |
ω | scaling parameter |
denotes test data |
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Decimation Factor | / | / | / | / |
---|---|---|---|---|
1 | 16.09 (1.23) | 16.10 (1.29) | 33.95 (2.70) | 48.80 (3.87) |
2 | 8.33 (1.01) | 8.23 (0.97) | 17.97 (3.14) | 15.52 (1.39) |
5 | 3.05 (0.29) | 3.19 (0.49) | 6.46 (0.58) | 3.93 (0.33) |
10 | 1.41 (0.13) | 1.41 (0.16) | 2.93 (0.27) | 1.53 (0.16) |
20 | 0.61 (0.14) | 0.63 (0.11) | 1.27 (0.15) | 0.64 (0.11) |
Decimation Factor | / | / | / | / |
---|---|---|---|---|
1 | 1.0 (0.0) | 1.0 (0.0) | 13.6 (1.2) | 5.0 (0.8) |
2 | 1.0 (0.0) | 1.0 (0.0) | 14.8 (1.2) | 5.1 (0.8) |
5 | 1.0 (0.0) | 1.0 (0.0) | 7.7 (1.2) | 4.9 (0.8) |
10 | 1.0 (0.0) | 1.0 (0.0) | 7.6 (1.3) | 4.8 (1.0) |
20 | 1.0 (0.0) | 1.0 (0.0) | 7.3 (1.4) | 4.6 (1.1) |
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Reichert, C.; Dürschmid, S.; Kruse, R.; Hinrichs, H. An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings. Computers 2016, 5, 5. https://doi.org/10.3390/computers5020005
Reichert C, Dürschmid S, Kruse R, Hinrichs H. An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings. Computers. 2016; 5(2):5. https://doi.org/10.3390/computers5020005
Chicago/Turabian StyleReichert, Christoph, Stefan Dürschmid, Rudolf Kruse, and Hermann Hinrichs. 2016. "An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings" Computers 5, no. 2: 5. https://doi.org/10.3390/computers5020005