Extracting Sensory Preferability from Motor Streams
Abstract
:1. Introduction
1.1. Reafference Principle and Sensory Input in Motor Stream
“Can we detect sensory variations within motor streams?”
“Is it possible to separate sensory and motor components automatically?”
“Could researchers develop methods to extract sensory information from continuous motor data?”
1.2. Interfaces in Need of “Understanding” the Preferability of Our Peripheral Nervous System
1.3. Baseline Research
1.4. Aims and Contributions
2. Experimental and Computational Methods
2.1. Participants
2.2. Experimental Task
Sensory Stimuli
2.3. Instrumentation
2.4. Data Processing
2.4.1. Extraction and Computation of Motor Stream
2.4.2. Micro-Movement Spikes
2.4.3. Micro-Movement Spikes Represented by Continuous Gamma Process
2.4.4. Tracking NSR and Predictive Regimes
2.4.5. Euclidean Distance of the Extreme Signatures
2.4.6. Rate of Change in Stochastic Transitions
2.4.7. Cumulative Information
2.4.8. Statistical Significance
3. Results
3.1. Investigating the Gamma Stochastic Signatures in Logarithmic Space
3.2. Observing Variations Within the Kinematic Chain of Each Participant
3.3. Expanding Investigations to the Four Moments of the Gamma Distribution
3.4. Exploring the Cumulative Space
3.4.1. Choosing Sensory Preferability Through Parameter Space 1
- P1: “Music 1”, “Music 2”, “Music Cl. Eyes 1”, and “Fav. music”
- P2: for all conditions
- P3: “Music Cl. Eyes 1” and “Fav. music”
- P4: “Music 2”, “Music Cl. Eyes 1”, and “Music Cl. Eyes 2”
- P5: “Fav. music”
- P6: “Cl. eyes 1” and “Cl. eyes 2”
- P7: “Music 1”, “Music Cl. Eyes 1”, and “Fav. music”
- P8: “Music 1”, “Cl. eyes 1”, “Cl. eyes 2”
- P9: for no condition
- P10: “Music 1”, “Cl. eyes 2”, and “Cl. eyes 2”.
3.4.2. Choosing Sensory Preferability Through Parameter Space 2
- P1: during condition “Fav. music”
- P2: during condition “Cl. eyes 2”
- P3: during condition “Fav. music”
- P4: during condition “Cl. eyes 2”
- P6: during condition “Cl. eyes 1”
- P7: during condition “Fav. music”
- P8: during condition “Cl. eyes 2”.
4. Discussion and Possible Applications
4.1. Metrics, Parameter Space, and Findings
4.2. Key Elements of the Study Design
4.3. Affecting Temporal and Spatial Features of the Motor Stream
4.4. Theory and Possible Applications
5. Conclusions
Supplementary Materials
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Condition | Description | Duration |
---|---|---|
# 1 | Walking | 5 min |
# 2 | Walking Cl. Eyes | 5 min |
Walking Open Eyes & Music | 5 min | |
# 3 | Music 1 | 2.17 min |
# 4 | Music 2 | 2.43 min |
Walking Closed Eyes & Music | 5 min | |
# 5 | Music Cl. Eyes 1 | 2.17 min |
# 6 | Music Cl. Eyes 2 | 2.43 min |
# 7 | Fav. Music | 5 min |
Walking | Cl.Eyes | Music 1 | Music 2 | M.Cl.E.1 | M.Cl.E.2 | Fav.M. | Mean | |
---|---|---|---|---|---|---|---|---|
P1 | 2.6153 | 3.0803 | 8.3027 | 9.9585 | ||||
P2 | 15.4041 | 5.0433 | 9.8127 | 7.5092 | 3.8996 | 6.1839 | 7.9351 | 7.96 |
P3 | 3.0795 | 9.9164 | 3.4311 | 2.2010 | 4.0664 | 2.7714 | 10.8285 | 5.18 |
P4 | 6.5625 | 3.3152 | 1.8821 | 1.3742 | 4.5432 | 1.8562 | 2.3375 | 3.12 |
P5 | 1.7314 | 2.1357 | 1.5971 | 1.5458 | 1.7745 | 1.9206 | 12.8226 | 3.36 |
P6 | 7.1734 | 2.5319 | 2.2215 | 1.3054 | 2.2047 | 1.7081 | 1.9690 | 2.73 |
P7 | 7.9360 | 1.6930 | 6.1193 | 2.3235 | 3.3887 | 2.1550 | 8.3185 | 4.56 |
P8 | 3.1501 | 6.2031 | 3.1024 | 4.2011 | 5.1410 | 6.3765 | 1.6493 | 4.26 |
P9 | 1.9820 | 4.3195 | 2.5957 | 1.4136 | 2.2465 | 1.5484 | 1.5303 | 2.23 |
P10 | 5.1849 | 4.2032 | 2.9588 | 2.3339 | 4.1045 | 6.4769 | 1.7299 | 3.86 |
KW Test | P1 | P2 | P3 | P4 | P5 |
p-values | 3.78 | 1.08 | 4.95 | 1.29 | 3.59 |
P6 | P7 | P8 | P9 | P10 | |
p-values | 2.25 | 2.54 | 1.59 | 4.39 | 6.99 |
KW Test | P1 | P2 | P3 | P4 | P5 |
Mean | 8.02 | 8.12 | 1.64 | 0 | 1.98 |
Variance | 2.79 | 1.33 | 4.86 | 8.6 | 2.60 |
Skewness | 7.81 | 6.18 | 2.48 | 0 | 2.62 |
Kurtosis | 5.20 | 2.56 | 6.61 | 1.38 | 1.35 |
P6 | P7 | P8 | P9 | P10 | |
Mean | 0 | 2.29 | 0 | 0 | 0 |
Variance | 4.05 | 2.09 | 1.73 | 1.88 | 2.88 |
Skewness | 0 | 7.97 | 0 | 0 | 0 |
Kurtosis | 6.83 | 1.33 | 4.82 | 3.59 | 3.64 |
Wilcoxon Rank-Sum Test | Ratio | Cum. Log. Gamma Slope |
---|---|---|
Music vs. No Music | >0.01 | |
Op.Eyes vs. Cl.Eyes | >0.01 |
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Kalampratsidou, V. Extracting Sensory Preferability from Motor Streams. Sensors 2025, 25, 2087. https://doi.org/10.3390/s25072087
Kalampratsidou V. Extracting Sensory Preferability from Motor Streams. Sensors. 2025; 25(7):2087. https://doi.org/10.3390/s25072087
Chicago/Turabian StyleKalampratsidou, Vilelmini. 2025. "Extracting Sensory Preferability from Motor Streams" Sensors 25, no. 7: 2087. https://doi.org/10.3390/s25072087
APA StyleKalampratsidou, V. (2025). Extracting Sensory Preferability from Motor Streams. Sensors, 25(7), 2087. https://doi.org/10.3390/s25072087