Using Neurofeedback from Steady-State Visual Evoked Potentials to Target Affect-Biased Attention in Augmented Reality
- Resource Type
- Conference
- Authors
- Huang, Xiaofei; Mak, Jennifer; Wears, Anna; Price, Rebecca B.; Akcakaya, Murat; Ostadabbas, Sarah; Woody, Mary L.
- Source
- 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Engineering in Medicine & Biology Society (EMBC), 2022 44th Annual International Conference of the IEEE. :2314-2318 Jul, 2022
- Subject
- Bioengineering
Training
Visualization
Protocols
Depression
Biology
Steady-state
Reliability
Attention
Augmented reality (AR)
Brain-computer interfaces (BCI)
Electroencephalogram (EEG)
Emotion
Steady-state visual evoked potentials (SSVEP)
- Language
- ISSN
- 2694-0604
Biases in attention to emotional stimuli (i.e., affect-biased attention) contribute to the development and mainte-nance of depression and anxiety and may be a promising target for intervention. Past attempts to therapeutically modify affect-biased attention have been unsatisfactory due to issues with reliability and precision. Electroencephalogram (EEG)-derived steady-state visual evoked potentials (SSVEPS) provide a temporally-sensitive biological index of attention to competing visual stimuli at the level of neuronal populations in the visual cortex. SSVEPS can potentially be used to quantify whether affective distractors vs. task-relevant stimuli have “won” the competition for attention at a trial-by-trial level during neuro-feedback sessions. This study piloted a protocol for a SSVEP-based neurofeedback training to modify affect-biased attention using a portable augmented-reality (AR) EEG interface. During neurofeedback sessions with five healthy participants, signifi-cantly greater attention was given to the task-relevant stimulus (a Gabor patch) than to affective distractors (negative emotional expressions) across SSVEP indices (p