New EEG technology reads brain waves to help paralyzed patients walk again

Scientists in Italy and Switzerland are developing non-invasive brain-monitoring technology to help patients with spinal cord injuries regain mobility.

Spinal cord injuries often block signals from the brain to the limbs, even when the brain and peripheral nerves remain healthy. This new research seeks to bypass the damaged section of the spinal cord to restore that connection.

The method uses an electroencephalogram (EEG) cap to record electrical brain signals when a patient attempts to move. These signals are decoded and sent to a spinal cord stimulator that triggers the necessary nerves.

Researchers said this approach offers a safer alternative to direct brain implants, which require complex surgery and carry a risk of infection.

However, EEG technology faces challenges in detecting leg movements because those signals originate deep within the brain. To address this, the team used machine learning algorithms to analyze the data.

The current system can distinguish between when a patient intends to move and when they are at rest, but it cannot yet identify specific types of movement. Future studies will focus on improving the accuracy of the algorithms.

This research marks a strategic pivot in the treatment of spinal cord injuries, shifting the clinical focus from the immense complexity of biological repair toward more pragmatic "bypass" solutions. Central to this transition is the move away from high-risk, invasive brain implants in favor of non-invasive electroencephalography (EEG) technologies. While surgical implants have demonstrated significant promise, they remain constrained by prohibitive costs, safety concerns, and limited patient accessibility. In contrast, wearable EEG caps offer a safer, more scalable alternative for widespread clinical adoption.

However, this transition involves a clear trade-off: prioritizing safety necessitates a compromise in signal precision. The primary technical bottleneck, as highlighted in the study, remains the difficulty of capturing deep-brain signals associated with lower-limb movement—a fundamental physical limitation of current EEG hardware. Consequently, the next breakthrough in the field is likely to be driven by software rather than hardware.

The integration of machine learning to decode weak and "noisy" neural signals is now the critical frontier. If algorithms can be refined to accurately distinguish specific intentions—such as standing, walking, or climbing stairs—the practical utility of this technology will increase exponentially. Although still in the nascent stages, this trajectory points toward a democratization of brain-computer interfaces (BCIs), evolving them from experimental interventions for a select few into a standard pillar of global rehabilitation therapy.