Brain-computer interface technology for chronic pain management

Chronic pain remains inadequately managed by pharmacological and interventional therapies, which carry risks including opioid-related complications and limited long-term efficacy. Brain-computer interfaces (BCI) have emerged as a non-pharmacological therapeutic approach, most commonly implemented using noninvasive modalities such as scalp EEG or fMRI, that aims to target neural activity patterns associated with chronic pain.

This review examines the principles underlying BCI systems and their application in chronic pain, including EEG-based neurofeedback targeting oscillatory activity, infraslow-frequency neurofeedback, a theta-controlled vibrotactile BCI, real-time fMRI-based neurofeedback, and emerging closed-loop neuromodulation systems. Advantages, limitations, and future directions are discussed.

BCI technology represents a promising, but still early-stage, approach for chronic pain management. Clinical translation will depend less on decoding accuracy than on addressing regulatory and reimbursement barriers, conducting adequately powered, sham-controlled trials, and implementing patient stratification. Convergence with artificial intelligence and closed-loop neuromodulation is promising, but algorithmic sophistication should not be equated with clinical applicability. Wearable platforms raise data privacy and transparency concerns. BCIs are likely to be adjuncts within multimodal pain management, not replacements for existing treatments. Preliminary evidence supports feasibility, but adequately powered, sham-controlled trials are needed to establish treatment-specific and durable efficacy.