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Title Page 2
Abstract 5
Contents 8
Nomenclature 20
Chapter 1. General Introduction 21
1.1. Introduction to Brain-Computer Interfaces 22
1.2. Backgrounds 23
1.2.1. ERP 23
1.2.2. ERP-BCI 24
1.3. Motivation 27
1.4. Barriers to the Application of BCI to Real-World 28
1.4.1. Environmental Complexity and Cognitive Overload 28
1.4.2. Types of External Interferences 28
1.5. Research Aim and Specific Objectives 29
1.6. Contribution 30
1.7. Thesis Structure 31
Chapter 2. Establishment of AR Integrated OnUne ERP-Based Brain-Computer Interface Platform for the Control of Home Appliances 33
2.1. Introduction 34
2.2. Materials and Methods 34
2.2.1. Participants 34
2.2.2. EEG Recordings 35
2.2.3. Experiment Setup 35
2.2.4. EEG Preprocessing and Online BCI 37
2.2.5. Evaluation 38
2.3. Results 38
2.4. Discussion 40
Chapter 3. External Interferences on the Use of ERP-Based BCI 43
3.1. Introduction 44
3.2. Study 1: Effects of Auditory Distraction 45
3.2.1. Materials and Methods 45
3.2.2. Results 50
3.2.3. Discussion 57
3.3. Study 2: Effects of Listening and Speaking Interruption 60
3.3.1. Materials and Methods 60
3.3.2. Results 70
3.3.3. Discussion 80
3.4. Conclusion 84
Chapter 4. ERP-Based BCI Using Adaptive Model in Interrupting Environments 86
4.1. Introduction 87
4.2. Study 3: Adaptive BCI Using Multi-Window Approach in Interrupting Environments 90
4.2.1. Materials and Methods 90
4.2.2. Results 100
4.2.3. Discussion 105
4.3. Study 4: Assessing Online Adaptive BCI Performance During Video-Watching Tasks 107
4.3.1. Materials and Methods 107
4.3.2. Results 112
4.3.3. Discussion 121
4.4. Conclusion 124
Chapter 5. Concluding remarks 125
5.1. Main findings 126
5.2. Implications for Future Research 127
5.3. Conclusion 128
References 129
Figure 1.1. Brain-computer mterface scheme 23
Figure 1.2. Interference effect from external environment 29
Figure 1.3. Purpose scheme of this dissertation 30
Figure 2.1. Experimental paradigm and stimulus sequence for each device control 36
Figure 2.2. AR-based BCI system components 37
Figure 2.3. Grand average ERPs across all participants of all EEG channels for TV, DL and EL 39
Figure 2.4. Online performance of BCIs 40
Figure 3.1. Experimental protocol 48
Figure 3.2. The distributions of the valence scores of high-, and low-valence stimuli used in the... 50
Figure 3.3. ERP graphs 52
Figure 3.4. Average accuracy of online ERP- BCI control for each stimulus condition (HV: High... 54
Figure 3.5. Average accuracy of online ERP- BCI control for each stimulus condition (HV: High... 54
Figure 3.6. The number of participants included in groups based on the difference from None... 55
Figure 3.7. The distributions of frontal alpha asymmetry (FAA) values under each emotional... 56
Figure 3.8. Changes in FAA from the None (no sound) condition to each stimulus condition (HV:... 56
Figure 3.9. Visual stimuli for ERP-BCIs and the experimental protocol 64
Figure 3.10. BCI performance and mental effort assessment 71
Figure 3.11. ERP waveform and topography 72
Figure 3.12. The peak amplitudes of ERP components and their correlations with the accuracy... 74
Figure 3.13. The variability of ERP latency and target classification scores 76
Figure 3.14. Occipital alpha power modulation level under different conditions 79
Figure 3.15. (A) Histograms representing the distribution of classification outputs under different... 80
Figure 4.1. Time-variant ERP difference, spatial filter weight, and accuracy 89
Figure 4.2. Multi-Window Adaptive Model 97
Figure 4.3. Offline adaptation scenario 98
Figure 4.4. Performance comparison of BCI configurations across different tasks 102
Figure 4.5. Performance comparison between Multi-Window Adaptive Model and other... 103
Figure 4.6. Analysis of model updates across different windows using validation set 104
Figure 4.7. Average change rate of spatial filters from multi-window model 105
Figure 4.8. Experimental design for assessing BCI performance during visual task interruptions 109
Figure 4.9. BCI Accuracy across sessions 113
Figure 4.10. NASA TLX and recognition test accuracy 114
Figure 4.11. ERP waveform comparison between control and watching conditions 115
Figure 4.12. ERP differences between control and watching conditions 115
Figure 4.13. Occipital alpha power changes across sessions and correlation between alpha power... 116
Figure 4.14. Performances of BCI model configurations across sessions 117
Figure 4.15. Differential performance analysis between MW-Adaptive and MW-Fixed models 118
Figure 4.16. Adaptation of spatial filter weight over sessions 120
Figure 4.17. ERP difference waveforms in Watching condition comparing participants with... 121
초록보기
A Brain-Computer Interface (BCI) is a system that interprets neural signals to control external devices or facilitate communication, offering significant benefits by bypassing muscle pathways. Despite extensive research, practical applications in daily environments remain limited due to several challenges. Especially, recent advancements in augmented reality (AR) and virtual reality (VR) technologies have broadened BCI applications but also introduced new challenges, such as increased cognitive load due to external interferences. This dissertation investigates the performance of event-related potential (ERP)-based BCIs in realistic settings, addressing challenges posed by external interferences and exploring solutions to enhance stability and usability.
The research is structured into three main chapters. The first chapter involves the development and evaluation of an AR integrated online ERP-based BCI platform aimed at controlling home appliances. This chapter elaborates on the integration of see-through user interface (UI) with augmented reality to enhance the user's interaction with the system in a real-world setting. The BCI system, connected through internet of things (IoT) and controlled via AR interfaces, was tested for its efficiency in manipulating common household appliances like televisions, door locks, and lights. The ERP-based BCI system implemented in this study served as the foundational platform for subsequent research endeavors.
The second chapter examined the effects of external interferences on the performance of ERP-based BCI systems, specifically categorizing them into distractions and interruptions. The findings from the two studies presented in this chapter underscore the significant impact of different types of interferences on the accuracy and reliability of BCI operations. The first study aimed to investigate the effects of auditory distractions withemotional conditions on ERP-based BCI performance. However, it was found that the influence of auditory distractions, including emotionally charged auditory stimuli, was not significant in modulating ERP amplitudes crucial for effective BCI control. The second study explored the impact of active verbal communication tasks on ERP-based BCI performance. It was observed that real-life tasks involving speaking act as substantial interruptions, requiring the user to reallocate cognitive resources away from the BCI task. These interruptions lead to a noticeable decrease in the ERP components' amplitudes including P3 and N2, which are essential for accurate BCI operations. In contrast, tasks like active listening and simple syllable pronunciation showed no substantial impact on BCI usability. Additionally, the study identified a correlation between occipital N2 and alpha power with BCI accuracy, indicating that speaking particularly diminishes visual attention, thereby adversely affecting BCI performance.
The third chapter explored the implementation of an adaptive model in ERP-based BCIs to enhance performance in interrupting environments. Two primary studies were conducted to evaluate the effectiveness of the adaptive model under different types of interruptions. The first study focused on the application of a multi-window adaptive approach to manage interruptions effectively. It was found that employing multiple time windows for analysis allowed the system to maintain higher classification accuracy even when users were subjected to speaking interruptions. This finding confirms the potential effectiveness of using this model in scenarios where interruptions are present. The second study investigated the performance of the multi-window adaptive BCI model during video-watching tasks, a common real-world scenario that involves significant visual interruptions. The results demonstrated that interruptions caused by video-watching decreased ERP amplitudes and consequently reduced BCI accuracy. However, employing the multi-window adaptive model significantly outperformed non-adaptive ones, maintaining accuracy and partially restoring performance levels despite interruptions.
Overall, this dissertation makes several key contributions to the field of BCI research. It provides a comprehensive analysis of how different types of external interferences affect ERP-based BCI performance, validates the effectiveness of adaptive models in mitigating these effects. The research represents a crucial step towards the broader adoption of BCIs in everyday life, demonstrating that with appropriate adaptations, BCIs can be made robust enough to function effectively in a wide range of real-world settings with various cognitive interferences.MARC 보기
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