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Prefrontal Asymmetry BCI Neurofeedback Datasets.
Fred Charles1, Caio De Castro Martins2, Marc Cavazza2
1Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom.
Frontiers in Neuroscience
|January 4, 2021
Summary
This study explores prefrontal cortex (PFC) asymmetry using functional near-infrared spectroscopy (fNIRS) neurofeedback (NF) as a Brain-Computer Interface (BCI). Individualized feedback enhances NF effectiveness in BCIs for affective neuroscience research.
Area of Science:
- Affective Neuroscience
- Brain-Computer Interfaces (BCI)
- Neurofeedback (NF)
Background:
- Prefrontal cortex (PFC) asymmetry is a key marker in affective neuroscience, linked to motivation, eating behavior, empathy, risk propensity, and depression.
- Functional near-infrared spectroscopy (fNIRS) is suitable for studying PFC asymmetry due to its artifact insensitivity.
- Brain-Computer Interface (BCI) paradigms emphasize individual baselines, sustained activation, and minimal training, using general population subjects without patient data.
Purpose of the Study:
- To present fNIRS datasets from three experiments utilizing PFC asymmetry neurofeedback (NF) within a Brain-Computer Interface (BCI) framework.
- To provide detailed descriptions of data formats, experimental protocols, and individualized success metrics for PFC asymmetry NF.
- To investigate the role of real-time, individualized visual feedback in enhancing participant engagement and BCI performance.
Main Methods:
- Utilized functional near-infrared spectroscopy (fNIRS) for real-time monitoring of prefrontal cortex (PFC) activity.
- Implemented a neurofeedback (NF) paradigm as a Brain-Computer Interface (BCI), focusing on left-asymmetric dorsolateral prefrontal cortex (DLPFC) activation.
- Collected data from three experiments involving participants interacting with continuous visual feedback based on their real-time brain activity.
Main Results:
- Datasets include detailed experimental protocols and individualized metrics for success scores based on baseline thresholds and reference tasks.
- Demonstrated the effectiveness of real-time NF in a BCI context, emphasizing the importance of adapting feedback to individual responses.
- Provided fNIRS datasets from studies on affective interactions with computer-generated narratives and heuristic search algorithms.
Conclusions:
- Real-time neurofeedback (NF) within a Brain-Computer Interface (BCI) paradigm offers valuable data for affective neuroscience research.
- Carefully designed protocols with individualized, adaptive visual feedback are crucial for maximizing the benefits of NF in BCIs.
- The presented fNIRS datasets contribute to the understanding of PFC asymmetry and its modulation via BCI-NF.
