Brain-Computer Interface (BCI) technology has advanced in recent years, bringing with it many potential benefits for individuals with disabilities or impairments. However, current MI-based (motor imagery-based) BCI frameworks face limitations in terms of their accuracy and practicality. The Enhanced Fusion Framework proposes three different ideas to improve the existing MI-based BCI frameworks.
What is the Enhanced Fusion Framework?
The Enhanced Fusion Framework is a proposed framework that aims to improve the accuracy and practicality of MI-based BCI systems. A BCI system is a direct communication pathway between the brain and an external device, such as a computer or a prosthetic device. MI-based BCI systems use motor imagery, or imagining movements without physically executing them, to control the device or machine.
At present, MI-based BCI systems require extensive training, are prone to error, and can be frustratingly slow. The Enhanced Fusion Framework aims to address some of these shortcomings by proposing three different approaches.
Three Proposals of the Enhanced Fusion Framework
Proposal 1: Combined Measurement of Electroencephalography (EEG) and Functional Near-infrared Spectroscopy (fNIRS)
The first proposal of the Enhanced Fusion Framework advocates for a combined measurement of both EEG and fNIRS. EEG is a technique that measures the electrical activity of the brain using electrodes attached to the scalp. fNIRS uses near-infrared light to measure changes in the brain's hemoglobin concentration, indicating changes in brain activity. By combining the two techniques, researchers can improve accuracy and allow for a more detailed picture of what is happening in the brain.
This proposal is significant because EEG has a high temporal resolution but low spatial resolution, while fNIRS has a high spatial resolution but low temporal resolution. Combining these two techniques could create an optimal balance between temporal and spatial resolution for MI-based BCI systems.
Proposal 2: Convolutional Neural Networks (CNNs)
Proposal two of the Enhanced Fusion Framework suggests the use of convolutional neural networks (CNNs) to improve the accuracy of MI-based BCI systems. A CNN is a type of neural network designed to process data with a grid-like structure, such as images. CNNs have already been successfully used in many computer vision applications and natural language processing.
The use of CNNs in MI-based BCI systems can help to improve their accuracy by allowing for automatic feature extraction. This means that the system can learn and identify specific patterns in the EEG signals that indicate motor imagery, without the need for manual extraction by a human operator. Additionally, CNNs may improve the speed at which the BCI system can operate, as they can process large amounts of data quickly.
Proposal 3: Active Attention Mechanism
The third proposal of the Enhanced Fusion Framework is the use of an active attention mechanism. This mechanism aims to improve the practicality of MI-based BCI systems by helping the user to focus their attention on the task at hand. The success of an MI-based BCI system relies heavily on the user's ability to generate a mental image of the desired movement or action.
The active attention mechanism works by providing the user with a visual or auditory cue that prompts them to focus on the mental task at hand. For example, the user may be asked to visualize a specific image or follow a pattern of sounds. By providing the user with a more engaging and interactive experience, the active attention mechanism can help to improve user engagement and overall success rates.
The Enhanced Fusion Framework proposes three innovative approaches to improve the accuracy, speed, and practicality of MI-based BCI systems. By combining EEG and fNIRS, using CNNs, and implementing an active attention mechanism, researchers hope to create an optimal balance between temporal and spatial resolution, improve accuracy through automatic feature extraction, and help users focus more effectively on the mental task at hand. The potential benefits of these proposals could lead to significant technological advancements in the field of BCI, ultimately helping individuals with disabilities or impairments to lead more independent and fulfilling lives.
