Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded signal during the BCI preprocessing stage of to achieve preferable results at the learning stage. This paper aims to present an overview on BCI different EEG brain signal recording artifacts and the methodologies to remove these artifacts from the signal focusing on different novel trends at BCI research areas.

No abstract

Controlling the surrounding world by just the power of our thoughts has always seemed to be just a fictional dream. With recent advancements in technology and research, this dream has become a reality for some through the use of a Brain Computer/Machine Interface (BCI/BMI). One of the most important goals of BCI is to enable handicap people to control artificial limbs. Some research proposed wireless implants that do not require chronic wound in the skull. However, the communications consume a high bandwidth and power that exceeds the allowed limits, 8-10mW. This study proposes and implements a modified version of realtime spike sorting for wireless BCI [4] that simplifies and uses less computation via an adaptive neural-structure; which makes it simpler, faster and power and area efficient. The system was implemented, and simulated using Modalism and Cadence, with ideal case and worst case accuracy of 100% and 91.7%, respectively. Also, the chip layout of 0.704mm2, with power consumption of 4.7mW and was synthesized on 45nm technology using Synopsys.

No abstract

No abstract