Brain-Computer Interface (BCI)-Based Feedback for Chronic Pain Management

Summary

The feasibility study proposed here will primarily examine the sensitivity of an electroencephalogram (EEG)-based Brain Computer Interface (BCI) in detecting significant differences in brain signals in patients with chronic low back pain (N=10), lower limb pain (N=10) and healthy controls (N=10) through perceived movements via a video and during actual movements. The BCI device has been approved for use in previous trials (e.g. NNI-IRB/07/001, DSRB Domain D/09/608, DSRB Domain D/10/072) and the safety and effectiveness of this non-invasive EEG-based BCI device validated through these trials. However, the validation has not been specific to its use in pain. Related to the primary objective of the study, we will develop and validate an adaptive and participant-specific pain detection and analysis program by exploring and identifying discriminative and robust patterns in spontaneous EEG from our study sample.

For the secondary objective, we will develop and validate a BCI and computer based pain and attention diversion training system with interactive audio-visual feedbacks for Phase 2 of the study. These feedbacks will inform the user about the current brain activation level and attention level, and guide the user in learning to modulate the EEG characteristics and develop skills to manage attention to alleviate perceived fear-related pains. The BCI system captures EEG signals and decodes the underlying brain states in relation to cognition and fear-related pain perception. Such decoded brain states are then presented to the participant in visual or other form to guide the participant to learn to regulate the brain states towards better pain management. For example, the participant may over a few sessions learn to focus on the visual feedback while inhibiting the brain function activity in relation to fear-related pain perception. With practice, the user is encouraged to achieve brain activity modulation without external feedback so fear-related pain can be reduced in realistic situations.

Conditions

• Pain, Chronic

Interventions

DEVICE

EEG-based BCI analysis of fear avoidance

Scalp EEG signals that are acquired and recorded from experiment 1 and 2 are analysed for healthy controls, participants with low back pain and participants with lower limb pain. Comparisons of signal patterns are made between groups. Assuming unique signal patterns are obtained for each groups, these signals are further analysed and used to develop a feedback brain training system to better manage pain and ideally reduce fear-avoidant behaviors towards pain.

Sponsors & Collaborators

• Institute for Infocomm Research

  collaborator OTHER

• Tan Tock Seng Hospital

  lead OTHER

Principal Investigators

• Su-Yin Yang, PhD · Tan Tock Seng Hospital

Study Design

Allocation

NA

Purpose

DIAGNOSTIC

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

21 Years

Max Age

65 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2017-06-05

Primary Completion

2019-03-31

Completion

2019-03-31

Countries

• Singapore

Study Locations