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Brain–Computer Interfaces

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Abstract

Brain–computer interfaces (BCIs) have emerged as a novel technology that bridges the brain with external devices. BCIs have been developed to decode human’s intention, leading to direct brain control of a computer or device without going through the neuromuscular pathway. Bidirectional brain–computer interfaces not only allow brain control but also open the door for modulating the central nervous system through neural interfacing. We review the concepts, principles, and various building blocks of BCIs, from signal acquisition, signal processing, feature extraction, feature translation, to device control, and various applications. The performance assessment and challenges of BCIs are also discussed. Examples of noninvasive BCIs are discussed to aid readers for an in-depth understanding of the noninvasive BCI technology, although this chapter is aimed at providing a general introduction to brain–computer interfaces.

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Acknowledgments

This work was supported in part by NIH AT009263, EB021027, EB006433, NS096761, MH114233, NSF CBET-0933067 (B.H.), as well as by NSF of China-90820304 (S.G.).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Bin He

  2. Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA

    Han Yuan

  3. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Jianjun Meng

  4. Department of Biomedical Engineering, Tsinghua University, Beijing, China

    Shangkai Gao

Authors
  1. Bin He
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  2. Han Yuan
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  3. Jianjun Meng
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  4. Shangkai Gao
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Corresponding author

Correspondence to Bin He .

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Editors and Affiliations

  1. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Bin He

Homework

Homework

  1. 1.

    Answer the following questions about the general aspects of BCI.

    1. (1.1)

      Define brain–computer interface (BCI) in your own words.

    2. (1.2)

      Describe at least 3 examples of BCI according to different signal resources and explain their pros and cons.

    3. (1.3)

      Describe what the unique challenges of BCI research are.

    4. (1.4)

      If you want to decode people’s imagery movement, which brain areas do you want to choose in order to build an EEG-based BCI?

  2. 2.

    Answer the following questions about the BCI signal acquisition.

    1. (2.1)

      What is the spatial resolution of noninvasive techniques such as EEG, MEG, and fMRI?

    2. (2.2)

      What is the spatial resolution of invasive techniques such as ECoG, multi-unit recording?

    3. (2.3)

      What is the temporal resolution of noninvasive techniques such as EEG, MEG, and fMRI?

    4. (2.4)

      For EEG-based BCI, does increasing the electrode number help to improve the decoding accuracy of motor imagination? Why?

    5. (2.5)

      Does the combination of different noninvasive modalities help to improve the decoding accuracy such as the simultaneous acquisition of EEG and fMRI? Please explain why?

  3. 3.

    Answer the following questions about the BCI feature extraction.

    1. (3.1)

      What kind of features could be extracted to decode the event-related potentials (ERP)?

    2. (3.2)

      Is it possible to decode the ERP in single trials? Please explain.

    3. (3.3)

      What kind of features could be used to decode the motor imagery–induced sensorimotor rhythms?

  4. 4.

    Answer the following questions about the SSVEP BCI.

    1. (4.1)

      What is the limitation to use a computer monitor as the display of the flicker in a steady-state visual evoked potential (SSVEP)–based BCI?

    2. (4.2)

      Download one of the examples (shared data, e.g., S1.mat, http://thubci.org/en/index.php?s=/home/index/nr/id/100/page/1.html) from the shared data in the ‘Wang et al (2016). A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1746-1752.’ Plot the power spectrum of electrode Oz from any one of the 40 targets in a single block and the average from all of the six blocks.

  5. 5.

    Answer the following questions about the motor imagery–based BCI.

    1. (5.1)

      Download one of the examples (shared data, e.g., S1_LR_20150130.mat) from the shared data in [14] and Readme file to learn the structure of the shared data.

    2. (5.2)

      Extract the multichannel signals of each trial; calculate the average feedback duration for the example session.

    3. (5.3)

      Calculate the average band power (8–13 Hz) of channel C3 and C4 over all of the left trials, respectively.

    4. (5.4)

      Calculate the average band power (8–13 Hz) of channel C3 and C4 over all of the right trials, respectively.

    5. (5.5)

      Compare the above average band power for left trials and right trials. Describe the difference.

  6. 6.

    What kinds of classification algorithms are commonly used in the EEG–based BCI?

  7. 7.

    Answer the following questions about robotic arm control using BCI.

    1. (7.1)

      Please explain what are the pros and cons to control a prosthetic or robotic arm by using different types of noninvasive BCI, such as SSVEP based and sensorimotor rhythm based.

    2. (7.2)

      What is the challenge for control of a high degree of freedom (DoF) robotic arm by noninvasive BCIs? Please describe your solution of controlling a high DoF robotic arm.

  8. 8.

    Answer the following questions about BCI applications.

    1. (8.1)

      What BCI could be used as a tool? Please describe at least three examples.

    2. (8.2)

      Please describe how BCIs could be used to induce tactile sensation neurofeedback.

  9. 9.

    Answer the following questions about the hybrid BCI.

    1. (9.1)

      Please describe an example of the hybrid BCI.

    2. (9.2)

      Please describe your solution of driving a wheelchair mounting with an assistive robotic arm to help drinking and eating via a hybrid BCI.

  10. 10.

    Answer the following questions about information transfer rate of BCI.

    1. (10.1)

      What is the state-of-the-art information transfer rate (ITR) of different types of noninvasive–based BCIs?

    2. (10.2)

      Please describe a possible solution of increasing the ITR of a noninvasive sensorimotor rhythm–based BCI and explain why it might work.

  11. 11.

    Answer the following questions about BCI development.

    1. (11.1)

      Please list three most important questions to be addressed in order to significantly improve the field of BCI.

    2. (11.2)

      Please discuss the potential of BCI application in the clinical field.

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He, B., Yuan, H., Meng, J., Gao, S. (2020). Brain–Computer Interfaces. In: He, B. (eds) Neural Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-43395-6_4

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