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Recognition of Mental Task with the analysis of long-range temporal correlations on EEG brain
oscillation
2012
The 3rd IEEE Biosignals and Biorobotics
conference (ISSNIP)
Christian F. Vega - Francisco Javier Ramirez-Fernandez
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YI. INTRODUCTION.
II. DETRENDED FLUCTUATION ANALYSIS (DFA).
III. MATERIAL AND METHODS.
IV. RESULTS
V. CONCLUSION
Christian F. Vega & Javier Ramirez
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The problem?
Recognizing cognitive states through EEG signals
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Why is our research? (Justified)
• Identify the potential of EEG signals.
• The EEG technology is cheaper in comparison with
FMRI (Functional magnetic resonance imaging).
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Objectives
This study has two goals:
• Analyze the presence of Long-Range Temporal
Correlation (LRTC) and scaling behavior with
Detrended Fluctuation analysis (DFA).
•Analyze the different statistics between the
differences mental tasks.
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STATE OF THE ART
• Recent studies reported that spontaneous neuronalactivity in the human brain reported long-rangetemporal auto-correlations (LRTC) [10].
• The LRTC was applied to study sleep stages [11].
• The literature report the presence of long-rangetemporal correlations and power-law scaling insignal neural brain while subject development mentalcognitive tasks [12].
• The DFA showed to be a useful tool for the diagnosisof Alzheimer's [3].
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II. DETRENDED FLUCTUATION ANALYSIS (DFA)
• Auto-correlations: calculated the correlation in the samesignal.
• To overcome these limitations is used Detrended FluctuationsAnalysis (DFA).
• If a temporal correlations � power-law.
Long-Range Temporal Correlation (LRTC).
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II. DETRENDED FLUCTUATION ANALYSIS (DFA)
• Temporal correlations � with a decay power-law.
• Indicates that the signal belongs to a system that has thecharacteristic of scale-free o scale behavior
similar processes at different scales.
• This phenomenon is known as self-similarity which is an essentialcharacteristic of fractals.
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II. DETRENDED FLUCTUATION ANALYSIS (DFA)
• The power-law is common: social networks, groups ofinfections, fractal, biological signals, etc.
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II. DETRENDED FLUCTUATION ANALYSIS (DFA)
•First, the amplitude of the oscillations is integrated.
•The integration of the signal Y (t) is divided into segments of size
•For each ,it is calculated the square root of the fluctuation.
•The parameter is defined as the scaling exponent that represent
the correlation property of the signal.
•This parameter is obtained by calculating the slope of the linear
relationship between and
•The DFA measures the long-range power-law correlations in a
variety of systems [14],[15].
a
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τ
log ( )F τ log( )τ
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II. EXPONENT SCALING PARAMETERS
• there is no correlation and the signal is an uncorrelatedsignal (random white noise).
• the signal contains power-law scaling behavior e long-range correlations.
– Large fluctuations amplitudes are likely to be followed by large amplitudes
• anti-correlated signal– The signal is anticorrelated and large amplitudes of fluctuations are likely to be
followed by small amplitudes and vice versa;
• is represented by a scale-invariant fractal process [12].
• correlation .– but not present the power-law.
1 a<
0 0.5a< <
0.5 1a< <
0.5a =
1a =
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III. MATERIAL AND METHODS
Christian F. Vega & Javier Ramirez
• The EEG data used here are available and provided by theColorado State University [23]. These data were collected byKeirn and Aunon [24]. The electrodes are:
• central part of the left hemisphere (C3).
• central part of the right hemisphere (C4).
• parietal lobe of the left hemisphere (P3).
• parietal lobe of the right hemisphere (P4).
• occipital lobe of the left hemisphere (O1).
• occipital lobe of the right hemisphere (O2).
Fonte:Adaptados de SANEI; CHAMBERS, 2007
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III. MATERIAL AND METHODS
Christian F. Vega & Javier Ramirez
• The EEG database contains records of 7 subjects for 10
• seconds of 5 mental tasks:
• Baseline (B), relaxed state.
• Multiplication (M), the subjects are conceiving multiplication.
• Letter-composing (LC), conceiving writing a letter.
• Rotation (R), imaging rotation of object.
• Visual-Counting (C), erasing and redrawing figures.
• Data were recorded for 10 seconds during each task andeach task was repeated five times per session. In the originalEEG dataset, there were seven subjects in the study, but onlyfour subjects were chosen (subject 1, subject 3, subject 4,and subject 5).
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III. MATERIAL AND METHODS
Christian F. Vega & Javier Ramirez
• The data were filtered with a notch filter to reject the
power line interference (60 Hz).
• The continum component (CD) was removed by the
Fourier Transform to reject the amplitude of the zero
frequency.
• The EEG signals were filtered with a fifth-order Elliptic
filter twice (filtering forward and backward), in order to
remove the effects of phase distortion and to eliminate the
artifacts of the signals to all electrodes.
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III. MATERIAL AND METHODS
• The Long-Range Temporal Correlation (LRTC) andscaling behavior was calculated with DetrendedFluctuation analysis (DFA).
• Finally, a statistical hypothesis test was used to analyze the differences between tasks.
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III. MATERIAL AND METHODS
• To analyze the difference between each of the tasks, the test hypothesis for non- parametric statistical data related, Wilcoxon signed-rank test was applied.
• Why?
• The data showed a non-normal distribution, which was confirmed with the Kolmogorov-Smirnov test.
• The same subjects were part of the whole study.
• Significance:
• When the statistical significance (p) are smaller than 0.05 (p <0.05), this represents a statistical difference between the two tasks with a 5% significance
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IV. RESULTS
Report a scaling exponent between 0.90 and 1.0083 range. LRTC
Report a scaling exponent between 1.0 and 1.1 range. LRTC
Brownian noiseChristian F. Vega & Javier Ramirez
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IV. RESULTS
Report a scaling exponent between 0.52 and 0.55 range. LRTC
Report a scaling exponent between 0.33 and 0.35 range
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IV. RESULTS
Report a scaling exponent between 0.16 and 0.19 range.
Report a scaling exponent between 0.02 and 0.02 range.
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IV. RESULTS
Tabla 5.1 –- the statistical analysis of the Wilcoxon signed rank hypothesis.
*com um nível de significância menor que 5%.
S (EEG signals).
D (Delta Band),
T (Theta Band).
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IV. RESULTS
*com um nível de significância menor que 5%.
• EEG � 3 pairs of mental tasks.
LRTC-scaling behavior.
• Delta � 5 pairs of mental tasks.
LRTC-scaling behavior. (Brownian noise)
• Theta � 5 pairs of the mental tasks.
LRTC-scaling behavior.
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• Total � 7 out of 10 pair of mental tasks.
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VI. CONCLUSION
•In summary, this study provides a support to the results withthe presence of LRTC in brain oscillation [3], as a significantmarker in human cognition.
•The EEG signals and the Theta Band reported the presenceof long-range power-law correlations and scaling behavior.
•The Delta Band reported the presence of long-rangecorrelations, but would approach the smoothnessof theBrownian noise.
•Our results reported significant differences (p< 0.05) in 7 out of 10 pair of mental tasks.
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VI. FUTURE APPLICATIONS
•Also that the DFA is a useful tool for discriminating mental tasks and it can be used to analyze:
• neural diseases.
• build brain-computer interface.
•understand the behavior of the brain.
•Alzheimer.
•Effects the drugs.
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Acknowledgment
•Supported by FAPESP grants 2011/22384-0and CNPq -Brazilian National Council of Scientific and TechnologicalDevelopment, Brazil.
•Christian Flores Vega like to thank Dr. Julien Noel for hissupport and review.
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Christian F. Vega & Javier Ramirez