Real‐time Neurofeedback Rewards Memory‐related Potentials in Older Brains

Background Electroencephalogram (EEG)-based neurofeedback (NF) allows reward of desired brain activity in milliseconds. Compared to frequency power-based approach during resting state EEG, event-related potentials (ERPs) based NF a task is technically challenging because the cost computation over time window. We have previously established that left-frontal memory-related discriminate normal cognitive aging from age- and education- matched older adults with risk mild impairment (MCI). To modulate brainwaves working memory retrieval towards healthier patterns, we develop an ERP-based protocol better potentials. Method The training developed on modified delayed match-to-sample also known as Bluegrass paradigm. During each trial, participant holds sample target image his/her responds whether subsequent Match or Nonmatch by pressing button. Real-time classification extracted ERP features analyzed after stimulus onset, dynamic machine-learning program then modifies turning it into vivid color (reward) leaving black-and-white (unrewarded). processing pipeline has been using ERPs without MCI. left frontal sites (F3 F7) were preprocessed re-referencing filtering 0.05 – 40 Hz band-pass filter. For participant, EEG 5 offline base trials 300-600ms window was used baseline. difference baseline versus single trial calculated determine will be colored at black-and-white. Result Our newly ERP-neurofeedback succeeded providing visual feedback fluctuation real time. designed fit individual due varied sensibility NF. Currently are examine feasibility effectiveness proposed measuring indicators before sessions. Conclusion new improve personalized retrieval. first step intervention for enhancing successful rewarding brains.