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A study on entropy as a measure of brain activation

A study on entropy as a measure of brain activation
Byeonggi Yu
DGIST Authors
Byeonggi YuSukho ParkSung Ho Jang
Sung Ho Jang
Issued Date
Awarded Date
brain activation, entropy, signal amplitude, beta value, mechanical impedance, fNIRS
brain activation, entropy, signal amplitude, beta value, mechanical impedance, fNIRS
Brain activation has been used to understand brain-level events associated with cognitive tasks or physical
tasks. As a quantitative measure for brain activation, we propose entropy in place of signal amplitude and
beta value, which are widely used, but sometimes criticized for their limitations and shortcomings as such
measures. To investigate the relevance of our proposition, we provided 22 subjects with physical stimuli
through mechanical impedance-restrained elbow extension-flexion active motion by using our exoskeleton
robot, measured brain activation in terms of entropy, signal amplitude, and beta value; and compared entropy
with the other two. The results show that entropy is superior, in that its change appeared in limited,
well-established, motor areas, while the signal amplitude and beta value changes appeared in a widespread
fashion, contradicting the modularity theory. When stimuli shifted from the rest state to the task state,
entropy exhibited a similar increase as the other two did. Entropy can predict an increase in brain activation
with task duration, while the other two cannot. In detail, as the unit size of task duration increased, entropy
increased by 1.09% and 1.19% at 780 and 830 nm wavelength data. Although entropy showed only a part
of the phenomenon induced by task strength, it showed superiority by showing a decrease in brain activation
that the other two did not show. In detail, as the unit size of the natural frequency defining the strength
of the task increased, the entropy decreased by 3.37% and 2.80% at 805 and 830 nm wavelength data.
Moreover, entropy was capable of identifying the physiologically important location.|뇌 활성화는 인지 작업 또는 신체 작업과 관련된 뇌 수준 이벤트를 이해하는 데 사용되고 있다. 우리는 뇌 활성화를 위한 정량적 척도로 신호 진폭과 베타 값 대신에 엔트로피를 제안하였다. 현재 신호 진폭과 베타 값은 널리 사용되고 있지만 한계들과 단점들로 인하여 때때로 정량적 척도로써 적절한지 비판을 받고 있다. 우리 제안의 관련성을 조사하기 위해 우리는 외골격 로봇을 사용하여 기계적 임피던스에 제한된 팔꿈치 신전-굴곡 능동 동작을 통해 22 명의 피험자에게 물리적 자극을 제공하고 엔트로피, 신호 진폭 및 베타 값 측면에서 뇌 활성화를 측정하였으며 엔트로피를 다른 둘과 비교하였다. 엔트로피의 변화가 제한적이고 잘 정립된 운동 영역에서 나타나는 반면 신호 진폭과 베타 값의 변화가 모듈성 이론과 모순되는 광범위한 방식으로 나타난다는 점에서 엔트로피의 우수함을 확인하였다. 자극이 휴식 상태에서 작업 상태로 변화할 때 엔트로피는 다른 두 지표와 유사한 증가된 경향을 보였다. 엔트로피는 작업 기간에 따른 뇌 활성화의 증가를 예측할 수 있지만 다른 두 가지는 예측할 수 없었다. 자세하게 확인하면 작업 지속시간의 단위 크기가 증가함에 따라 780 및 830 nm 파장 데이터에서 엔트로피가 1.09%, 1.19% 증가하였다. 비록 엔트로피는 작업 강도에 의해 유도되는 뇌 활성화 현상의 일부분만을 보여주었지만 나머지 두 지표가 보여주지 않은 뇌 활성화의 감소를 보여 우월성을 보여주었다. 자세하게 확인하면 작업 강도를 정의하는 고유 주파수의 단위 크기가 증가할수록 805 및 830 nm 파장 데이터에서 엔트로피가 3.37%, 2.80% 감소하였다. 게다가 엔트로피는 생리적으로 중요한 위치를 식별할 수 있었다.
Table Of Contents
Ⅰ. Introduction 1
1.1 Importance of quantifying brain activation 1
1.1.1 Importance of quantifying brain activation in clinical fields 1
1.2 Conventional approach to quantify brain activation and problems 2
1.2.1 Signal amplitude 2
1.2.2 Beta value 3
1.3 Background of introducing entropy to quantify brain activation 3
1.4 Introducing mechanical impedance-restrained motion to modulate task intensity 4
1.4.1 Significance of introducing mechanical impedance in clinical fields 5
1.5 Summary of the experiment 5
Ⅱ. Methods 6
2.1 Overall Configuration 6
2.1.1 Hardware overview 7
2.1.2 Software overview 8
2.2 Stimuli Generation 10
2.2.1 Robot system 10
2.2.2 Control method 12
2.2.3 Visual guidance feedback system 13
2.3 Measurement 15
2.3.1 Functional Near-Infrared Spectroscopy (fNIRS) system 15
2.3.2 Subject information 16
2.3.3 Experiment protocol 17
2.4 Data Analysis 23
2.4.1 Data processing 23
2.4.2 Entropy calculation 24
2.4.3 Signal amplitude calculation 24
2.4.4 Beta value calculation 24
2.4.5 Statistical analysis 24
2.4.6 Brain activation map 26
Ⅲ. Results 27
3.1 Control Verification 27
3.1.1 Contact test 27
3.1.2 Impedance error 29
3.2 Entropy changes owing to physical stimuli 31
3.2.1 Entropy difference between rest and task 31
3.2.2 Entropy change over rest duration 32
3.2.3 Relationship between task duration and entropy 33
3.2.4 Relationship between task strength and entropy 35
3.3 Comparison of entropy change with signal amplitude and beta value 35
3.4 Comparison of entropy change with brain activation map 36
Ⅳ. Discussion 37
4.1 Desired impedance is achieved 37
4.2 Brain activation could be quantified by entropy 37
4.2.1 Rest state and task state 37
4.2.2 Task duration 38
4.2.3 Task strength 38
4.3 Comparison with signal amplitude and beta value 40
4.3.1 Physical definition aspect 40
4.3.2 Experiment results aspect 41
4.4 Physiological interpretations 42
4.5 Reason for not considering damping ratio 43
4.6 Consideration of entropy calculation 43
Ⅴ. Conclusion 44
Department of Robotics and Mechatronics Engineering
Related Researcher
  • 박석호 Park, Sukho 로봇및기계전자공학과
  • Research Interests Biomedical Micro/Nano Robotics; Biomedical Devices and Instruments
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Department of Robotics and Mechatronics Engineering Theses Ph.D.


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