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NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 2

by EScholar: Electronic Academic Papers for ScholarsFebruary 16th, 2024

Too Long; Didn't Read

NOIR presents a groundbreaking BRI system enabling humans to control robots for real-world activities, but also raises concerns about decoding speed limitations and ethical risks. While challenges remain in skill library development, NOIR's potential in assistive technology and collaborative interaction signifies a significant step forward in human-robot collaboration.

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Authors:

(1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; [email protected];

(2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; [email protected];

(3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; [email protected];

(4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; [email protected];

(5) Chen Wang, Department of Computer Science, Stanford University;

(6) Wensi Ai, Department of Computer Science, Stanford University;

(7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University;

(8) Shreya Gupta, Department of Computer Science, Stanford University;

(9) Yilun Hao, Department of Computer Science, Stanford University;

(10) Ruohan Gao, Department of Computer Science, Stanford University;

(11) Anthony Norcia, Department of Psychology, Stanford University

(12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University;

(13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University.

Table of Links

Abstract & Introduction

Brain-Robot Interface (BRI): Background

The NOIR System

Experiments

Results

Conclusion, Limitations, and Ethical Concerns

Acknowledgments & References

Appendix 1: Questions and Answers about NOIR

Appendix 2: Comparison between Different Brain Recording Devices

Appendix 3: System Setup

Appendix 4: Task Definitions

Appendix 5: Experimental Procedure

Appendix 6: Decoding Algorithms Details

Appendix 7: Robot Learning Algorithm Details

Appendix 2: Comparison between Different Brain Recording Devices

We use the EGI NetStation EEG system which uses rapid application 128-channel saline-based EGI SensorNets. Here we justify our choice of using non-invasive, saline-based EEG as the recording device for brain signals. A comparison of different brain reading devices (gel-based EEG, dry EEG, MEG, fMRI, fNIRS, implant) and their advantages and disadvantages are shown in Table 5, using our device as the baseline. Two noticeable alternatives are functional magnetic resonance imaging (fMRI) and invasive implants. fMRI measures the small changes in blood flow that occur with brain activity, which has a very high spatial resolution hence fine-grained information such as object categories and language [31] can be decoded from it. But fMRI suffers from low temporal resolution, and the recording device is extremely costly and cannot be used in daily scenarios. Brain implants have a very good signal-to-noise ratio and have great potential. However, the main concern is that it requires surgery to be applied, and health-related risks are not negligible.