Presentation on theme: "Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals HCC 741- Dr. Amy Hurst Wajanat Rayes."— Presentation transcript:
Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals HCC 741- Dr. Amy Hurst Wajanat Rayes
outline Introduction Overview of Brain-Computer and Brain-Machine Interfaces Noninvasive Assistive Brain-Machine Interfaces in Paralysis The proposed prototype system Conclusion References
Introduction There are different ways of humans interaction with machines such as speech, gestures, and eye movements interaction. These communication channels depend on a functional motor system. As more people getting severe damage in their motor system which resulting in paralysis and inability to communicate, brain-machine interfaces (BMI) become promising field to overcome this dependence. (BMI) translate electric or metabolic brain activity into control signals and People with complete paralysis can learn to use their brain waves to control and interact with assistive machines
Overview of Brain-Computer and Brain- Machine Interfaces It was discovered that brain waves contain information about cognitive states and can be functionally specific. The idea is to use such signals for brain control of assistive machines became a major driving force for the development- brain-computer or brain machine interfaces (BCI/BMI) Neural or metabolic brain activities can be recorded from sensors inside or outside the brain.
Overview of Brain-Computer and Brain- Machine Interfaces- Cont. As neural brain activity can be recorded from sensors inside or outside the brain, BCI/BMI is categorized as invasive or noninvasive systems. In noninvasive systems, activity can be recorded from outside the skull using electro- or magnetoencephalography (EEG/MEG).
Noninvasive Assistive Brain-Machine Interfaces in Paralysis The proposed prototype system is to include other biosignals into a system to detect user’s intentions, and assure reliability of the system. Particularly to integrate eye movements using electrooculography (EOG) or eye tracking into prosthetic control.
The proposed prototype system - Cont. The prototype system allows asynchronous BCI/BMI control while solving the reliability issue by using eye tracking, EOG, and computer vision-based object recognition.
The proposed prototype system – Cont. A computer equipped with a 3D camera recognizes objects placed on a table. The system detects when the user fixates any of the objects recognized as graspable, for example, a cup or ball. Once an object is fixated with the eyes, the BCI/BMI mode switches on, detecting whether the user wants to grasp the object. A robotic hand performs the grasping motion.
The motion becomes interrupted if the user does not fixate the object anymore as measured by eye tracking and EOG. This assures that no action of the system depends exclusively on brain wave control that might be susceptible to inaccuracies. Such system, promises to overcome many limitations of brain control alone, mainly the reliability issue. As trauma or stroke can affect motor and body functions very differently in each individual, proper and fast calibration for inclusion into seamless BNCI control is often impeded. The proposed prototype system – Cont.
Conclusion BCI/BMI systems promise to enhance applicability of assistive technology in humans with a damaged motor system. Many reasons suggest that using the combination of brain waves with other biosignals might entail many attractive solutions to control assistive, noninvasive technology even after severe damage of the central or peripheral nervous system.
References de Almeida Ribeiro, P. R., Brasil, F. L., Witkowski, M., Shiman, F., Cipriani, C., Vitiello, N.,... & Soekadar, S. R. (2013). Controlling assistive machines in paralysis using brain waves and other biosignals. Advances in Human-Computer Interaction, 2013, 3.
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