Using Neurofeedback from Steady-State Visual Evoked Potentials to Target Affect-Biased Attention in Augmented Reality

Area of Science:

• Neuroscience
• Psychology
• Biomedical Engineering

Background:

• Affect-biased attention contributes to depression and anxiety.
• Previous interventions to modify attention biases have shown limitations in reliability and precision.
• Electroencephalogram (EEG)-derived steady-state visual evoked potentials (SSVEPS) offer a precise, temporally-sensitive neural index of attention.

Purpose of the Study:

• To pilot a protocol for SSVEP-based neurofeedback training to modify affect-biased attention.
• To test the feasibility of using a portable augmented-reality (AR) EEG interface for neurofeedback.
• To assess the efficacy of SSVEP indices in quantifying attention allocation during neurofeedback.

Main Methods:

• A pilot study involving five healthy participants.
• Utilized a portable AR EEG interface for SSVEP-based neurofeedback.
• Participants underwent training sessions focusing on attention to task-relevant stimuli over affective distractors.

Main Results:

• Significantly greater attention was directed towards task-relevant stimuli (Gabor patch) compared to affective distractors (negative emotional expressions) across SSVEP indices (p<0.0001).
• SSVEP indices demonstrated excellent internal consistency (Guttman split-half coefficient = 0.97).
• The protocol showed feasibility and potential for modifying attention biases.

Conclusions:

• Findings suggest SSVEP neurofeedback is a viable method for quantifying and potentially modifying affect-biased attention.
• The developed SSVEP-guided AR neurofeedback protocol warrants further investigation.
• This approach holds promise for future interventions targeting adolescent girls at high risk for depression.