Researchers at Russia's Higher School of Economics (HSE) have found that reducing delay in neurofeedback (NFB) significantly increases training efficiency.
NFB is a type of biofeedback that uses real-time measures of brain activity, such as electroencephalography (EEG, or electrical activity), to teach people how to regulate their brain waves. It is used to alleviate symptoms of various neurological and mental health disorders, including autism, depression, and seizures.
Signals picked up by an EEG are presented to the subject as an audio or visual stimulus (watching a film or a column height, for example), which they can then modulate in any direction according to their mental state. Rewards, such as a brighter screen when watching a film or a shifting column size, for positive changes in their brain waves allegedly reinforce healthier thinking patterns.
Previous findings from HSE found that in most modern NFB approaches, the reinforcement signal lags behind the neuronal activity, making learning and NFB therapy less effective. To reduce this lag, researchers developed a new way to filter and streamline the EEG signal. They then tested it on 40 participants split into four groups- each receiving NFB for a task with different delays in reinforcement signal- 240 ms being the shortest, followed by 250 ms and 500 ms.
In doing so, the researchers found that participants in the group with the minimum delay required less time than those in other groups to achieve optimum levels of parietal alpha rhythm power, something linked to mental coordination and feeling relaxed. The researchers further found that these changes remained after training as well.
"This method gave us access to a previously unknown area of brain interaction with minimal latency in responses from the external system. It allows the brain to perceive an artificially created feedback loop as part of its own neural network. This is a qualitative leap that opens a new era in neurofeedback paradigm research," says Alexey Ossadtchi, Director of the Centre for Bioelectric Interfaces at HSE University.