Human Computer Interaction: Brain Computing Interface
Brain Computer Interface (BCI) technology is a powerful communication tool between users and systems. It does not require any external devices or muscle intervention to issue commands and complete the interaction. The research community has initially developed BCIs with biomedical applications in mind, leading to the generation of assistive devices. They have facilitated restoring the movement ability for physically challenged or locked-in users and replacing lost motor functionality.
COMMUNICATION AND CONTROL
Brain computer interface (BCI) systems build a communication bridge between human brain and the external world eliminating the need for typical information delivery methods. They manage the sending of messages from human brains and decoding their silent thoughts.
USER STATE MONITORING
Early BCI applications have targeted disabled users who have mobility or speaking issues. Their aim was to provide an alternative communication channel for those users. But later on, BCI enters the world of healthy people as well.
Brain computer interfaces have contributed in various fields of research as they are involved in medical, neuron ergonomics and smart environment, neuron marketing and advertisement, educational and self-regulation, games and entertainment, and Security and authentication fields.
Healthcare field has a variety of applications that could take advantage of brain signals in all associated phases including prevention, detection, diagnosis, rehabilitation and restoration.
PREVENTIONVarious consciousness level determination systems along with their brain-related studies have been developed. The attentiveness influences of smoking and alcohol on brain waves.
DETECTION AND DIAGNOSIS
Mental state monitoring function of BCI systems has also contributed in forecasting and detecting health issues such as abnormal brain structure, Seizure disorder, Sleep disorder, and brain swelling.
REHABILITATION AND RESTORATIONMobility rehabilitation is a form of physical rehabilitation used with patients who have mobility issues, to restore their lost functions and regain previous levels of mobility.
NEURO ERGONOMICS AND SMART ENVIRONMENT
As previously mentioned, deploying brain signals is not exclusive to the medical field. Smart environments such as smart houses, workplaces or transportations could also exploit brain computer interfaces in offering further safety, luxury and physiological control to humans’ daily life.
NEURO MARKETING AND ADVERTISEMENT
Marketing field has also been an interest for BCI researches. The research in has explained the benefits of using EEG evaluation for TV advertisements related to both commercial and political fields. BCI based assessment measures the generated attention accompanying watching activity.
EDUCATIONAL AND SELF-REGULATION
Neuron feedback is a promising approach for enhancing brain performance via targeting human brain activity modulation. It invades the educational systems, which utilizes brain electrical signals to determine the degree of clearness of studied information.
GAMES AND ENTERTAINMENT
Entertainment and gaming applications have opened the market for nonmedical brain computing interfaces. Various games are presented like in where helicopters are made to fly to any point in either a 2D or 3D virtual world.
SECURITY AND AUTHENTICATION
Security systems involve knowledge based, object based and/or biometrics based authentication. They have shown to be vulnerable to several drawbacks such as simple insecure password, shoulder surfing, theft crime, and cancelable biometrics.
BCI SYSTEM COMPONENTS
BCI system consists of four basic components. They include signal acquisition, signal preprocessing, feature extraction, and classification. Feature extraction component generates the discriminative characteristics for the improved signal, decreasing the size of the data applied to the classification component.
Measuring brain generated oscillations is one of the main components in any BCI based system. It reflects the voluntary neural actions generated by user’s current activity. Various methods for signal acquisition have been studied. It is the BCI application and the category of its intended users that decides the proper signal acquisition method and its measured phenomena.
Invasive recording methods implant electrodes under the scalp. They measure the neural activity of the brain either Intra Cortically from within the motor cortex or on the cortical surface.
Intra Cortical acquisition technique represents the most invasive method. It is planted under the cortex surface of the brain. It can be achieved using single electrode, or array of electrodes that measure the action signals out of individual neurons.
ECOG is a recording method that brings a less invasive option while at the same time preserves the advantages of invasive approach. It involves implanting electrode grids or strips over the cortex surface through a surgical operation.
These recording methods follow the approach that does not require implanting of external objects into subject’s brain. Thus it avoids the surgical procedures or permanent device attachment needed by invasive acquisition.
It is a non-invasive method that measures magnetic fields produced by electrical currents occurring naturally in the brain. The magnetic signal outside of the head is currently acquired only using the superconducting quantum interference device (SQUID).
FUNCTIONAL MAGNETIC RESONANCE IMAGING (FMRI)
FMRI detects the changes in blood flow which are related to neural activity in the brain using the device. Thus it helps mapping activities to the corresponding used brain areas which is known as source localization problem. It depends on the fact that any usage of brain part requires the increase of incoming blood flow.
FUNCTIONAL NEAR-INFRARED SPECTROSCOPYFIRS is a noninvasive technique that measures blood dynamic in the brain in order to detect the neuronal activity. It uses light in the near-infrared range to determine the blood flow. It has the advantage of providing high spatial resolution signals.
ELECTROENCEPHALOGRAM (EEG)Electroencephalography (EEG) is the recording of electrical activity along the scalp through measuring voltage fluctuations accompanying neurotransmission activity within the brain.
IMPACT OF BRAIN COMPUTING
Establishing the communication interface using brain signals has faced a lot of challenges. They can be categorized as technical and usability. Technical challenges are concerned with the system obstacles specially those regarding EEG features characteristics. Usability challenges describe the limitations affecting the level of human acceptance. Placing beneficial impact on today’s technology so far.
CHALLENGES FACED BY BRAIN COMPUTING INTERFACEUSABILITY CHALLENGES
They express the limitations facing the user acceptance of BCI technology utilization. They include the issues related to the training process necessary for classes’ discrimination. Information transfer rate (ITR) is one of the system evaluation metrics that combines both performance and acceptance aspects.
Training the user is a time-consuming activity either in guiding the user through the process or in the number of recorded sessions. It takes place either in preliminary phase or in the classifier calibration phase.
INFORMATION TRANSFER RATE
It is the widely used evaluation metric for command BCI systems. It depends on the number of choices, the accuracy of target detection, and the average time for a selection. Thus compared to imagery BCI, selective attention strategies achieve higher ITR as their offered choices are larger/
They are issues related to the recorded electrophysiological properties of the brain signals which include non-linearity, non-stationary and noise, small training sets and the companying dimensionality curse.
The brain is a highly complex nonlinear system in which chaotic behavior of neural ensembles can be detected. Thus EEG signals can be better characterized by nonlinear dynamic methods than linear methods.
NONSTATIONARITY AND NOISENon stationary attribute of electrophysiological brain signals represents a major issue in developing a BCI system. It originates a continuous change of the used signals over time either between or within the recording sessions. The mental and emotional state background through different sessions can contribute in EEG signals variability.
SMALL TRAINING SETSThe training sets are relatively small, since the training process is influenced by usability issues. Although heavily training sessions are considered time consuming and demanding for the subjects.
HIGH DIMENSIONALITY CURSE
In BCI systems, the signals are recorded from multiple channels to preserve high spatial accuracy. As the amount of data needed to properly describe different signals increases exponentially with the dimensionality of the vectors, various feature extraction methods have been proposed.
Several solutions have been investigated to confront and limit the influence of the previously mentioned technical issues. They are spread over various BCI system components. The following sections explain some employed methods for improving the performance of BCI based systems.
Preprocessing in either spatial, time or frequency domains has contributed in enhancing the signal caused especially by external factors. Improving the signal to noise ratio of EEG signals is done by increasing the signal level and/or decreasing the noise level.
SEPARABILITY OF MULTIPLE CLASSES
Machine learning techniques are employed to translate user’s intent into a valid choice. They discriminate and identify the selected class. They have been used, for example, to overcome some limitations associated with small training sets, single trial, and also the variability between sessions and within individual sessions.
LINEAR DISCRIMINANT ANALYSISLD
A is deployed to find the linear combinations of feature vectors which describe the characteristics of the corresponding signal. LDA seeks to separate two or more classes of objects or events representing different classes.
SUPPORT VECTOR MACHINE
SVM is an algorithm that belongs to a category of classification methods which use supervised learning to separate two different classes of data. It exploits a discriminant hyper plane to identify classes like does LDA.
BRAIN COMPUTING RELATED TO HCI
Advances in cognitive neuroscience and brain imaging technologies have started to provide us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that can monitor some of the physical processes that occur within the brain that correspond with certain forms of thought. Researchers have used these technologies to build brain-computer interfaces (BCIs), communication systems that do not depend on the brain’s normal output pathways of peripheral nerves and muscles. In these systems, users explicitly manipulate their brain activity instead of using motor movements to produce signals that can be used to control computers or communication devices. Using this information, systems can dynamically adapt themselves in order to support the user in the task at hand.
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