Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain Computer/Machine Interface (BCI/BMI). BCI has great potential for solving many handicapped people's problems via neural-controlled implants. However, current BCI/BMI solutions are not practical nor safe, which limits its usability  . Researchers have proposed a wireless implant that does not require chronic open wounds in the patient’s skull. Conversely, the heavy communication traffic between the implant chip, the external appliance and the complex clustering algorithms usually consume large amounts of energy that restrict the implementation. The majority of the BCI signal processing can be solved via spike sorting techniques for neural detection. Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80% of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor impaired patients, especially due to the fact that many medical personnel are not qualified for EEG signal analysis. Therefore, a portable automated detection and monitoring solution is in high demand. Thus, in this study an adaptive and simplified VLSI architecture for BCI Spike Sorting and detection that reduces the circuit complexity and power consumption based on neural fingerprint and Artificial Immune System algorithms (AIS) was developed. Two example systems are proposed. First, a usable implant safe adaptive online low-power BCI chip for artificial limb control, “System I” functions via an invasive neural fingerprint identifying technology with 93% accuracy via a 0.704 mm² chip that consumes 4.7mW of power. Second, a system of a wireless wearable adaptive for early prediction of epilepsy seizures, “System II” works via minimally invasive wireless technology paired with an external control device (e.g., a doctors’ smartphone), with a higher than standard accuracy (71%) and prediction time (14.56 sec). These novel architectures have not only opened new opportunities for daily usable BCI implementations, but they can also restore an active life, or simply save a life by helping to prevent a seizure’s fatal consequences.
|Advisor:||Bayoumi, Magdy A.|
|Commitee:||Fakih, Afef, Kumar, Ashok, Maida, Anthony|
|School:||University of Louisiana at Lafayette|
|School Location:||United States -- Louisiana|
|Source:||DAI-B 78/04(E), Dissertation Abstracts International|
|Subjects:||Neurosciences, Computer Engineering, Computer science|
|Keywords:||Brain computer interface, EEGs, Low power, Vlsi|
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