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칼만필터 EEG 보행디코딩을 위한 신경망과 기계학습을 통한 알고리즘 연구
칼만필터 EEG 보행디코딩을 위한 신경망과 기계학습을 통한 알고리즘 연구
  • 저자Sho Nakagome, Trieu Phat Luu, Yongtian He, Akshay Sujatha Ravindran & Jose L. Contreras-Vidal
  • 출판사아진
  • 출판일2020-07-22
  • 등록일2020-12-21
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책소개

Pre-processing pipelines for different offline experiments
are represented in Fig. 1. The base pipeline is selected such that they can easily be used in an online real-time
decoding scheme18. An H-infinity algorithm was used to specifically remove eye blinks, eye motions, amplitude
drifts and recording biases simultaneously23. The parameters of the H-infinity algorithms were kept the same as
the real-time decoding. Peripheral channels were removed as they typically contain many artifactual components.
The signals were then bandpass filtered using a 4th order butterworth filter. Although the frequency range was the
same, this is one of the differences compared to the real-time decoding as the real-time implementation utilized
finite impulse filter and the phase shift was expected. To this point, all processing was done through a MATLAB
script, which is also provided in the open-sourced repository. Additionally, before each experiment, the signals
were z-scored for each channel

목차

제 1편 : MATLAB 기본편
1. MATLAB 기본사용편  003
1.1 MATLAB 시작하기  003
 명령창(command Window)에서의 입력  005
 도움말(Help)의 이용  007
1.2 입력 오류의 수정  008
 계산의 중지  009
 MATLAB 종료하기  009
1.3 연산과 변수의 할당  009
 연산자 우선순위  011
 내장함수  012
1.4 데이터의 표현  013
1.5 변수의 처리  015
 변수 이름  015
 clear 명령어  016
 특수변수와 정수  017
 whos 명령어  017
1.6 벡터와 행렬  018
 벡터  018
 행렬  023
 스크린 출력과 억제  024
1.7 랜덤(Random)수와 복소수  025
 랜덤 수  025
 복소수  027
1.8 기호를 이용한 연산  028
 기호식에서의 치환  029
1.9 코드 파일  030
 스크립트 코드 파일  030
 코멘트의 추가  032
 함수 코드 파일  033
 사용자 정의함수  036
1.10 간단한 그래프의 생성  037
 ezplot을 이용한 그래프  037
 plot을 이용한 그래프  039
 3차원 그래프  042
1.11 MATLAB과 엑셀(Excel)의 접속  043
 엑셀 데이터 불러오기  043
 데이터 가져오기 옵션  046
 스크립트 생성 옵션  049
 함수 생성 옵션  049
 생성된 데이터를 엑셀파일로 저장하기  050







제 2편 : 연구논문 
An empirical comparison of neural networks and machine learning
algorithms for EEG gait decoding

1. Introduction 51
2. Methods 52
3. Results 56
4. Discussion 64
5. References 66

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