Atmel Microcontroller Analysis

ii) Remote control

For ensuring the safety of the user this system provides an additional type of controlling other than BRI and it is Remote control, which means robot is controlled by an external person. The switching of the control system is optional which is done by a mode selector. Simplified block diagram of remote control is shown in Fig.5. Atmel microcontroller is used in the design process of remote control. These types of microcontroller provide justified technology, an affluent efficiency in integrated product designs, and breaking innovation. The technology of capacitive touch in microcontroller supports to develop navigators like buttons. Furthermore, Atmel microcontrollers provide wireless and security support. The liquid
The major steps in the BRI involve signal acquisition and signal processing, where Signal processing consist of preprocessing, feature extraction and translation. The schematic of the BRI components are as shown in Fig.6.

i) Suitable Brain Signals

The P300 (P3), SSVEP and ERS/ERD waves are the most suitable brain signals that are used to design an EEG based BRI. P300: The decision making of a person triggers to generate a waveform is called P3 wave, therefore it is considered as an event related potential (ERP) [6]-[8]. SSVEP: The corresponding signals of visual stimulus at particular frequency (ranging from 3.5 Hz to 75 Hz) is used to generate electrical activity of brain is treated as Steady state visual evoked potentials [9], [10]. ERD/ERS: Event-related synchronization and event-related de-synchronization is based on the energy changes of EEG signal due to mental task [11].

ii) Signal Acquisition and Processing

The EEG signals from the scalp are collected using silver/silver chloride (Ag/AgCl) electrodes are the very cost effective method. If gel is required between the electrode and scalp then this kind of electrode is called “wet” electrode. Use of this electrode needs very long time and it is uncomfortable to
Preprocessing helps to avoid the artifact from the EEG signal. Preprocessing can be possible with a filter (such as low-pass, high-pass, band-pass and notch filter) that eliminates the Non-physiological (such as line noise) and physiological artifacts. On the basis of thinking, the feature extraction classifies the frequency of preprocessed brain signal into a particular class of activity, whether it belongs to forward, backward, right, left, and or to stop. Finally these frequencies undergo a translation process that translates them into useful commands or code for accessing the physical device like