How does the brain control motor function? The brain is "hardwired" with connections, which are made by billions of neurons that make electricity whenever they are stimulated. The electrical patterns are called brain waves. Neurons act like the wires and gates in a computer, gathering and transmitting electrochemical signals over distances as far as several feet. The brain encodes information not by relying on single neurons, but by spreading it across large populations of neurons, and by rapidly adapting to new circumstances. Motor neurons carry signals from the central nervous system to the muscles, skin and glands of the body, while sensory neurons carry signals from those outer parts of the body to the central nervous system. Receptors sense things like chemicals, light, and sound and encode this information into electrochemical signals transmitted by the sensory neurons. And interneurons tie everything together by connecting the various neurons within the brain and spinal cord. The part of the brain that controls motor skills is located at the ear of the frontal lobe. How does this communication happen? Muscles in the body's limbs contain embedded sensors called muscle spindles that measure the length and speed of the muscles as they stretch and contract as you move. Other sensors in the skin respond to stretching and pressure. Even if paralysis or disease damages the part of the brain that processes movement, the brain still makes neural signals. They're just not being sent to the arms, hands and legs. A technique called neurofeedback uses connecting sensors on the scalp to translate brain waves into information a person can learn from. The sensors register different frequencies of the signals produced in the brain. These changes in brain wave patterns indicate whether someone is concentrating or suppressing his impulses, or whether he is relaxed or tense. NEUROPROSTHETIC DEVICE: A neuroprosthetic device known as Braingate converts brain activity into computer commands. A sensor is implanted on the brain, and electrodes are hooked up to wires that travel to a pedestal on the scalp. From there, a fiber optic cable carries the brain activity data to a nearby computer. PRINCIPLE: "The principle of operation of the BrainGate Neural Interface System is that with intact brain function, neural signals are generated even though they are not sent to the arms, hands and legs. These signals are interpreted by the System and a cursor is shown to the user on a computer screen that provides an alternate
"BrainGate pathway". The user can use that cursor to control the computer, just as a mouse is used." BrainGate is a system developed by the bio-tech company in in conjunction with the Department of Neuroscience at . The device was designed to help those who have lost control of their , or other bodily functions, such as patients with (ALS) or . The , which is implanted into the patient and converts the intention of the user into computer commands. NEURO CHIP: Currently the chip uses 100 that 'hear' firing in specific areas of the brain, for example, the area that controls arm movement. The activity is translated into electrically charged signals and are then sent and decoded using a program, which can move either a robotic arm or a computer cursor. According to the Cyberkinetics' website, three patients have been implanted with the BrainGate system. The company has confirmed that one patient () has a spinal cord injury, whilst another has advanced . In addition to real-time analysis of neuron patterns to relay movement, the Braingate array is also capable of recording electrical data for later analysis. A potential use of this feature would be for a to study seizure patterns in a patient with . Braingate is currently recruiting patients with a range of neuromuscular and neurodegenerative conditions for pilot in the United States. WORKING: Operation of the BCI system is not simply listening the EEG of user in a way that let’s tap this EEG in and listen what happens. The user usually generates some sort of mental activity pattern that is later detected and classified. PREPROCESSING: The raw EEG signal requires some preprocessing before the feature extraction. This preprocessing includes removing unnecessary frequency bands, averaging the current brain activity level, transforming the measured scalp potentials to cortex potentials and denoising. Frequency bands of the EEG :
. Band [Hz] [_V] Location Alpha (_) 8-12 10 -150 Occipital/ Parietal regions 9-11 varies Precentral/ regions Beta (_) 14 -30 25 typically frontal regions Theta (_) 4-7 varies varies Delta (_) <3 varies varies DETECTION: The detection of the input from the user and them translating it into an action could be considered as key part of any BCI system. This detection means to try to find out these mental tasks from the EEG signal. It can be done in time-domain, e.g. by comparing amplitudes of the EEG and in frequency-domain. This involves usually digital signal processing for sampling and band pass filtering the signal, then calculating these time -or frequency domain features and then classifying them. These classification algorithms include simple comparison of amplitudes linear and non-linear equations and artificial neural networks. By constant feedback from user to the system and vice versa, both partners gradually learn more from each other and improve the overall performance. CONTROL: The final part consists of applying the will of the user to the used application. The user chooses an action by controlling his brain activity, which is then detected and classified to corresponding action. Feedback is provided to user by means e.g. when typing with virtual keyboard, letter appears to the message box etc. TRAINING: The training is the part where the user adapts to the BCI system. This training begins with very simple exercises where the user is familiarized with mental activity which is used to relay the information to the computer. Motivation, frustration, fatigue, etc. apply also here and their effect should be taken into consideration when planning the training procedures. BIO FEEDBACK: The definition of the biofeedback is biological information which is returned to the source that created it, so that source can understand it and have control over it. This biofeedback in BCI systems is usually provided by visually, e.g. the user sees cursor moving up or down or letter being selected from the alphabet. A boon to the paralyzed -Brain Gate Neural Interface System visual
and were able to reconstruct recognisable scenes and moving objects. In the 1980s, Apostolos Georgopoulos at Johns Hopkins University found a mathematical relationship between the (based on a function). He also found that dispersed groups of neurons in different areas of the brain collectively controlled motor commands but was only able to record the firings of neurons in one area at a time because of technical limitations imposed by his equipment. There has been rapid development in BCIs since the mid-1990s. Several groups have been able to capture complex brain motor centre signals using recordings from (groups of neurons) and use these to control external devices, including research groups led by Richard Andersen, , Phillip Kennedy, , and Andrew Schwartz. Diagram of the BCI developed by Miguel Nicolelis and collegues for use on Rhesus monkeys Later experiments by Nicolelis using rhesus monkeys, succeeded in and reproduced monkey reaching and grasping movements in a robot arm. With their deeply cleft and furrowed brains, rhesus monkeys are considered to be better models for human than owl monkeys. The monkeys were trained to reach and grasp objects on a computer screen by manipulating a joystick while corresponding movements by a robot arm were hidden.The monkeys were later shown the robot directly and learned to control it by viewing its movements. The BCI used velocity predictions to control reaching movements and simultaneously predicted hand gripping force. Other labs that develop BCIs and algorithms that decode neuron signals include from Brown University, Andrew Schwartz from the University of Pittsburgh and Richard Andersen from Caltech. These researchers were able to produce working BCIs even though they recorded signals from far fewer neurons than Nicolelis (15–30 neurons versus 50–200 neurons). Donoghue's group reported training rhesus monkeys to use a BCI to track visual targets on a computer screen with or without assistance of a joystick BCI). Schwartzss's group created a BCI for three-dimensional tracking in virtual reality and also reproduced BCI control in a robotic arm. CONCLUSION: The idea of moving robots or prosthetic devices not by manual control, but by mere “thinking” (i.e., the brain activity of human subjects) has been a fascinated approach. Medical cures are unavailable for many forms of neural and muscular paralysis. The enormity of the