Literature Review Plan: BCIs in Controlling Non-Driving and Driving Robotics
1. Introduction
The primary focus of this literature review is to investigate how brain-computer interfaces (BCIs) have been applied to control both non-driving and driving-related robotics, with a special emphasis on predicting human intention. This review will cover EEG-based BCIs as the primary method of interest, as well as other non-invasive approaches where relevant to robotic control.
The scope of this review will include applications such as autonomous vehicles, medical robots, industrial robots, and teleoperated machines. To ensure a thorough analysis, the review will utilize the PRISMA methodology for conducting a systematic review and meta-analysis.
2. Scope and Focus
Primary Focus:
The primary focus is on the application of brain-computer interfaces (BCIs) for controlling both non-driving robotics and driving-related robotics. The review will emphasize approaches that incorporate intention prediction using BCI systems.
Key Themes:
BCI in Autonomous and Non-Autonomous Vehicles: Applications include autonomous, semi-autonomous cars, and rovers, examining how BCI is utilized for navigation and control. BCI in General Robotic Control (Non-Vehicle): A broader exploration of BCI-driven control in medical, industrial, teleoperated machines, and drones. Comparative Analysis: A comparison between BCI-driven control methods and traditional control systems, with a focus on performance, accuracy, and applicability. Vehicle control (autonomous and semi-autonomous) Predicting intention of the driver or robot operator during control tasks 3. Key Concepts
BCI Techniques:
The review will focus primarily on non-invasive EEG-based BCIs. Other non-invasive techniques, such as functional near-infrared spectroscopy (fNIRS), will be explored briefly where applicable to robotic control.
Types of Robotics:
Non-Driving Robotics: This includes medical robots, drones, teleoperated systems, and industrial robots. Driving-Related Robotics: The review will specifically focus on vehicles such as cars, ground vehicles, and rovers. Control Strategies:
The review will explore how BCIs are used to control navigation, decision-making, and direct movements in robots. A classification of different control strategies will be presented, with special emphasis on direct movement control and how it relates to predicting the user's intentions.
Signal Processing and Feature Extraction:
A discussion will be included on how neural signals are processed and translated into control commands through the use of machine learning (ML) and deep learning (DL) techniques for feature extraction, classification, and control.
Training Methods:
Both supervised and unsupervised learning methods will be analyzed, as they pertain to training robotic systems using BCI input for control.
4. Literature Search Strategy
Scope:
The review will focus on the last 10 years of research to provide a comprehensive overview, given the rapid advancements in the fields of BCI and robotics. However, adjustments may be made if the bulk of relevant work is found within the last 5 years due to recent technological developments.
Databases:
The following academic databases will be used to source literature:
Search Keywords:
To identify relevant literature, the following combinations of keywords will be used:
"EEG-based control of robots" "Brain-computer interface and vehicle control" "BCI for autonomous driving" "Deep learning," "Machine learning in BCIs," "Signal processing for BCI" Specific Combinations: "BCI, robot, EEG, prediction, intention," "BCI, EEG, prediction, intention, driving," "BCI, robot, EEG, prediction, intention, control systems." Review Papers:
Existing review papers will be examined for an overview of the field. These will serve as a foundation for identifying key themes, while also ensuring that new contributions to the review address gaps in current research—particularly regarding intention prediction in robotic control systems.
5. Organization of Literature
Categorization:
The literature will be organized based on the following categories:
Driving Robotics: This section will focus on BCIs in controlling vehicles, including autonomous and semi-autonomous cars, as well as ground vehicles and rovers. Non-Driving Robotics: This will cover BCI-driven control in medical robots, drones, industrial robots, and teleoperated systems. Control Strategies: The literature will be grouped based on different control methods, with a focus on direct movement control and predictive modeling. Challenges: Identifying common challenges faced in the implementation of BCI for robotics control, including signal reliability, control latency, and user training. Innovations: Highlighting novel approaches, emerging technologies, and advancements in signal processing and ML/DL applications for BCIs. 6. Writing the Review
Structure:
The literature review will be structured as follows:
Define BCI technology and its application in controlling robots. Discuss the motivation for using BCIs, particularly in predicting user intention. PRISMA methodology will be applied to ensure a systematic and transparent selection process for the literature. Criteria for inclusion and exclusion will be clearly outlined. BCI for Non-Driving Robotics: Summarize key studies, techniques, and applications. BCI for Driving Robotics: Focus on vehicle control, particularly autonomous driving, and the challenges of real-world environments. Comparative Analysis: Compare different approaches (BCI-driven vs. traditional methods). Predicting Intention: Discuss methods for predicting user intention, with an emphasis on machine learning and deep learning techniques. Challenges and Future Directions: Identify the major challenges in current BCI applications. Suggest future research areas (e.g., improving signal reliability, addressing control latency). Summarize findings and provide actionable insights for future research. 7. PRISMA Methodology
A systematic approach using the PRISMA framework will be adopted to ensure transparency in the literature selection and review process. This will include:
Identification: Extensive literature searches will be conducted across multiple databases using predefined keywords. Screening: The titles and abstracts of identified studies will be screened to eliminate irrelevant literature. Eligibility: Full-text articles will be assessed against inclusion/exclusion criteria to select final papers. Inclusion: A final selection of articles will be included in the review, following the PRISMA guidelines. 8. Timeline and Milestones
Week 1-2: Conduct initial literature searches and gather relevant papers. Week 3-4: Organize literature into the identified categories and begin drafting. Week 5-6: Focus on comparative analysis and intention prediction sections. Week 7-8: Finalize the draft, proofread, and revise.
1. Citation Information:
Journal and Year of Publication DOI or link to the full text 2. Research Objectives:
What was the primary goal of the study? Does the study focus on non-driving or driving-related robotics? Is the focus on intention prediction, or is the BCI applied for other types of control? 3. BCI Type and Approach:
EEG-based or other non-invasive methods? (Specify if it uses other techniques like fNIRS, MEG, etc.) Type of BCI Paradigm: Motor imagery (MI), P300, SSVEP, or other? Signal Processing Techniques: Feature extraction methods (e.g., CSP, ICA), classification algorithms (SVM, CNNs, etc.). Accuracy and Results: What are the key performance metrics (accuracy, precision, etc.) for intention prediction or control? 4. Robotics Application:
Type of Robot: Autonomous vehicles, teleoperated machines, industrial or medical robots? Control Method: Is the BCI used for direct control (e.g., moving a robotic arm) or higher-level control (e.g., setting the intention for autonomous systems)? Use Case: Describe the specific use case or experiment (e.g., autonomous driving, robotic surgery, industrial task automation). Role of Intention Prediction: How is human intention predicted and utilized in the robotic control system? 5. Human Participants and Study Design:
Number of Participants: How many human subjects were involved? Demographics: Any notable characteristics (age, gender, skill level)? Experimental Setup: How were experiments conducted? (e.g., real-world vs. simulation) Duration of Study: How long did the experiment last, and how was data collected? 6. Key Findings and Contributions:
What are the key findings related to BCI performance and human intention prediction? Were there any unique contributions or new methodologies introduced in the paper? Did the study address driving-related robotics or non-driving robotics? Was the method successful in either application? 7. Challenges and Limitations:
What challenges were encountered in terms of BCI performance or integration with robotics? Are there any limitations noted in the study (e.g., small sample size, need for improvement in signal processing)? Are there recommendations for future work? 8. Relevance to Your Review:
How does the study contribute to your literature review? Is the focus on intention prediction? Does the methodology or findings relate directly to your topic? Any specific points to incorporate into your systematic analysis or meta-analysis? 9. Study Quality:
Evaluate the quality of the study: robustness of methodology, clarity of results, and any biases. Is the paper a review, original research, or a meta-analysis? (Use this information for quality assessment under PRISMA.) 
