Title

Transformer-based Explainable Artificial Intelligence (XAI) for the Classification of Brain fNIRS Signals

Alternative Title

뇌 기능적 근적외선 분광법(fNIRS) 신호 분류를 위한 변압기 기반 설명 가능 인공지능(XAI)

DGIST Authors

Jim Knebel ; Jae Youn Hwang ; Kyung Hwan Jin

Advisor

황재윤

Co-Advisor(s)

Kyung Hwan Jin

Issued Date

2023

Awarded Date

2023-02-01

Citation

Jim Knebel. (2023). Transformer-based Explainable Artificial Intelligence (XAI) for the Classification of Brain fNIRS Signals. doi: 10.22677/THESIS.200000658414

Type

Thesis

Description

Vision Transformer, Time series, Explainable Artificial Intelligence (XAI), Layerwise Relevance Propagation, Classification, fNIRS, Brain Computer Interface,비전 트랜스포머(ViT), 시계열, 설명가능 인공지능(XAI), 계층별 관련성 전파(LRP), 분류, 기능적 근적외선 분광법(fNIRS), 뇌-컴퓨터 인터페이스(BCI)

Table Of Contents

Ⅰ. Introduction 1
1.1 Motivation 1
1.2 Background 5
1.2.1 Functional Near-Infrared Spectroscopy in BCI 5
1.2.2 XAI for inspecting model output 8
1.2.3 Transformer and Vision Transformer Model Architectures 10
II. Material and Methods 12
2.1 Datasets 12
2.2 Data Preprocessing & Model Configurations 16
2.3 XAI: Layerwise Relevance Propagation 20
2.4 Locality Based Patch Embedding 24
2.5 Reconstructing & Combining Attention Maps 26
III. Results 28
3.1 Patch embedding method : Performance Comparison 28
3.2 Patch embedding method : Qualitative Evaluation 31
3.3 Patch Embedding Perturbation Experiments 33
3.4 Comparing XAI methods 39
IV. Discussion 42
V. Conclusion 45
VI. References 46

URI

http://hdl.handle.net/20.500.11750/45744
http://dgist.dcollection.net/common/orgView/200000658414

DOI

10.22677/THESIS.200000658414

Degree

Master

Department

Department of Electrical Engineering and Computer Science

Publisher

DGIST

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