Brain Computer Interface Projects

Brain computer interface is a kind of communication system in which brain signals are considered foremost to act for human’s decision making. Generally, BCI is the technology that has the capacity to procure the brain signals and convert the same into several commands. Are you surfing for an article by means of brain computer interface projects then this is absolutely meant for you guys!!! 

Implementation of Brain Computer Interface Projects

What is meant by Brain Computer Interface?

The combination of artificial intelligence and human intelligence together makes the brain computer interfaces better by performing some of the tasks and gives significant outputs. This article is fully contented with the concepts that are essential for framing effective brain computer interface projects. To be honest this handout is definitely going to impress you guys. Now let us begin this article with an overview of the brain computer interface. The tasks are mentioned in the below section,

  • Tasks- Artificial Intelligence
    • Environ Understanding
    • Big Data Learning
    • Big Data Interpretation
    • Attaining Goals
  • Tasks- Human Intelligence
    • Understanding
    • Learning
    • Concepts Framing
    • Analytical Thinking

These are the various tasks done by both artificial and human intelligence. The collaboration of the intelligence may offer the abilities to systems such as problem-solving, decision or policy-making, and searching for the targets. This is how systematic intelligence and biological intelligence makes the BCI system better.

There are so many devices are supporting this technology. For example, armless people can be implanted with artificial hands in fact; these hands are controlled by the BCI systems to perform the actions directed by the brain signals.

Major Processes of Brain Computer Interface 

They do several processes such as

  • Brain signal acquisition
  • Signal analyzing & signal to convert into commands in order to communicate with the external devices.

Neuromuscular pathways are not used in the BCI Systems. The main objective of the brain computer interface systems is to help out the physically challenged people who are suffering from some disabilities known as injuries in spinal cords, stroke, cerebral palsy & lateral sclerosis, and so on.  In fact, there are some of the components that have been involved to run the BCI systems. In the upcoming section, we are actually going to list the various components of the brain computer interface projects. Come on, guys!!! Let us discuss the same with crystal clear points. 

What are the Components of a Brain Computer Interface?

  • Acquisition of Signals
    • Sensor modalities are being used to measure the brain signals
    • For instance, fMRI (metabolic) & intracranial electrodes (electrophysiologic)
    • Electronic processing is determined by the signal leverages
    • These signals are filtered out from the noises/artifacts accompanied
    • Finally, brain signals are computerized & propagated to the external devices
  • Feature Extraction
    • It is the process of extracting necessary signals from the raw brain signals
    • Then it formulates the contents into a proper one for feature translation
    • It is having the resilient correlation bonding between the user intensions
    • Human brain motions are oscillatory & temporary for investigation
    • ECoG & EEG based BCI systems extract the time-oriented brain activities
    • It is also responding to the power bands, latencies, cortical neurons & amplitudes
    • Brain signals are measured by biological & environmental artifacts/noise removals
  • Feature Translation
    • The processed brain signals are converted into relevant commands
    • Output devices will receive the commands which are intended by users
    • For instance, the frequency band’s power reduction can be translated as an upward shift
    • These should have the resilient adaptation feature according to the changes that occur
    • Changes may be in the forms of impulsive and also signaling feature variations
    • External devices can be controlled over by the effective translation features
  • External Device Output
    • External devices are operated by the feature translation commands
    • Commands such as computerized arm functions, cursor switches & letter picking
    • Finally, the control loop is closed by intimating the device function feedbacks

The above listed are the 4 major components gets comprised in the BCI technologies. In fact, without these components, the BCI system cannot run properly. In other words, these components can be said as key factors of the BCI. On the other hand, BCI systems are categorized into 2 major types. For your better understanding, we are actually going to enumerate the same with brief notes. 

Major 2 Types of BCIs

  • Asynchronous Brain computer interface
    • Pros of Asynchronous BCIs
      • It is an effortless interfacing system for the users
    • Cons of Asynchronous BCIs
      • It is recurrent in nature thus demands high computations
      • It is liable to the artifacts that arises during the eye movements
  • Synchronous Brain computer interface
    • Pros of Synchronous BCIs
      • Controlling noises is every easy for the users
      • Time is predetermined for the eye blinks/movements
      • An effective system which has the intelligence towards the user commands
    • Cons of Synchronous BCIs
      • Systems don’t permit any user to interpret the commands

These are the 2 major types or classifications of BCI systems. Further, we hope that you are getting the pros and cons of these types. If you are subjecting to any doubts or dilemmas in these areas you could approach our technicians to get abundant interesting facts in the determining aspects. Moreover, BCI signal recordings are acquired by some of the key techniques. Yes, dears the next section is all about the brain signal recording techniques for the BCI systems for the ease of your understanding.  

Brain Signal Recording Techniques for BCIs

  • Functional Near-Infrared Imaging
  • Functional Magnetic Resonance Imaging
  • Metabolic Signals
  • Magnetoencephalography
  • Electroencephalography
  • Electrocorticography
  • Intracortical Electrode Arrays
  • Electrical & Magnetic Signals

The listed above are some of the techniques which are practiced by the world-class engineers presented in the world. In fact, our researchers in the institute are always concerned about the student’s welfare that why they are stating the familiar and popular methods handled by the developers. However, the BCI systems are facing some issues in progressing. Next, we can have the section with the big issue in the brain computer interface. 

What is the Big Issue in Brain Computer Interface?

Among many issues in Brain Computer Interface Projects, feature extraction is one of the biggest issues let’s check what is it?

“Feature Extraction is an important step for classification of brain signals into the specific classes. Henceforth extracting the optimum set of features must be a significant demand in the brain computer interface. For emotion classification, emotion-based features must be chosen optimal than the other types of features”

Feature extraction is subject to various processes one among them is pattern recognition which segregates each and every pattern into the relevant features. Besides, it is not only extracting the resembling features but also extracts the features which are having dissimilarities. In this regard, let us have the section about the properties that are required for feature extraction in the brain computer interface. Come let’s have the quick insights.

 What are Properties are required for Feature Extraction in BCI?

  • Modeling the appropriate set of features is one of the biggest issues in BCI
  • Signals are acquired even from the instantaneous brain signaling sources
  • The interest of the brain is twinned with the various signal spaces & time/duration
  • Using the simplest bandpass filter for extracting band power will not result in optimum

The above listed are the various properties used for the feature extraction processes. In fact, features are measured and imitated by the brain signal properties which are surrounded with the discriminative data in which different types of signals can be categorized.  On the other hand, there are so many tools and software that have been used by the technicians in which we are going to illustrate some of the significant tools that are handpicked by world-class engineers for the ease of your understanding with our phd assistance. Come this is the right time to know about the toolkits and software used in brain computer interface projects. 

Tools and Software for Brain Computer Interface

  • xBCI
    • xBCI is compatible with every operating system
    • It has the Graphical User Interface & Diverse Functional modalities like,
      • Signal acquisition
      • Signal storages
      • Arithmetical functions
      • Signal preprocessing
      • Data communication
      • Data graphics
      • Empirical protocols
      • Responses & feedbacks
  • OpenVIBE
    • It is the digital reality-based neurofeedback tool which is scripted in C++
    • Plugins features accompanies the new modules with existing frameworks
    • TCP/IP stack is used for the EEG data communication among servers & users
  • rtsBCI
    • It is a Simulink & Matlab based toolkit intended for the Windows OS
    • Newfangled modules are facilitated with either m/c or graphical improvements
  • FieldTrip
    • These toolboxes investigate the MEG & EEG information
    • It supports with the both online and offline analytical competences
    • Enlarging prevailing modules & experiment designing can be done by BrainStream3
  • BF++
    • BF++ stands for Body Language Framework which is written in C++
    • It has the capability of implementing the prescribed BCI models
    • It also offers the ability to optimization, analysis & implementation
    • Biological signals can be performed by the BF++ compilers such as NPXLab
  • BCI2000
    • BCI2000 is oriented with the Borland C++ & comprises module types in 4,
      • Source
      • Operator
      • Signal Processing
      • Application
    • Online system of the BCI can be possible by interconnecting these modules
    • In other words, these are standalone modules of BCI which uses TCP/IP stack

These toolkits are used to eradicate the issue that arises in the BCI system. For this, we need to select the appropriate tools and software with proper considerations. As a matter of fact, our researchers in the concern are skilled in handpicking the relevant toolkits. By conducting so many researches on the concern we know each and everything that is needed for every approach.  If you are facing any issues with the suitable tool you are welcome to have our opinions. As this article has the subject of brain computer interface projects, we felt that it would be nice to present the same section here. Come on guys let us move on to the next section. 

Latest Research Topics in Brain Computer Interface Projects

  • BCI based Speech Recognition
  • Eye Tracker / Gaze Identification
  • Heart Rate Detection
  • BMI & Hybrid Identification
  • EMG Identification
  • BCI & Hybrid Identification
  • BCI Hypothesis Modeling
  • BCI System Valuations
  • Innovated & Improved BCI

The aforementioned are some of the latest project topics according to the BCI system. At this time, we wanted to illustrate one of our simple project implementation for the ease of your understanding in the upcoming section. In fact, we are actually going to show you the pinch of salt. Let’s get into the next section. 

Recent Brain Computer Interface Projects

Research Area – Hybrid BCI based Mental Stress Detection

Project Title – Bi-GRU & Attention Matrix- Siamese CapsuleNet based Combined NIRS & EEG for Early Mental Stress Detection  

Datasets Used

  • NIRS and EEG are the key datasets used in this proposed project idea
  • These datasets are intellectual in mining the actions ranging from the top, bottom, left, right, front & back
  • They are contented with the details of signal & stress variations from 29 female & males
  • Stress leverages are labeled and classified into separate levels of stresses
  • The main objective of this dataset is to differentiate the stress levels & control ability

In fact, we are going to illustrate to you the project’s flow with their well-organized steps for the ease of your understandingCome let us try to understand the phases involved in the proposed project with brief explanations.

  • Preprocessing
    • Extrinsic Noise Removal (Distortion)
    • Intrinsic Noise Removal (Eye Blink)
    • Signal Smoothing (Normalize)

A morphological bandpass filter is used to eliminate the extrinsic noises/artifacts. Band subsets are applied with the morphological filters. Besides, linear discriminant analysis (LDA) is widely used to abolish the eye movement/blinks noises as well as the time variable phase significantly enhances the speed of convergent.

While removing the artifacts, it is very important to note the size of the steps. Cubic spatial interpolation methodologies are used to perform the brain signal smoothing functionalities. In addition, they equalize the high-resolution frequencies & low-resolution frequencies.

  • Feature Extraction
    • NIRS & EEG Signal Extraction
    • Multiple Direction Inputs
    • Irrelevant Feature Reductions

After the preprocessing brain signals are being extracted from the EEG & NIRS in this project. Bi-GRU-based attention matrix-siamese CapsuleNet is the newfangled technique used in this feature extraction step. It is deep learning-based Gated Recurrent Units (GRU) which progresses in a sequential manner. NIRS & GRU are the two main sources of the inputs. Inattention layer, the vector’s weighted values are matched & projected by feature extraction.

  • Classification
    • Labeling Relevant Features
    • Class Predictions & Stress Levels

Later the feature extraction step, the features are selected according to the GWO, WOA, GA & PSO techniques and forwarded to the capsule layers for exactly predicting the classes. They classify the stress levels ranging from low, modest & peak by the application of siamese CapsuleNet.

Finally, outcomes are evaluated by the validation techniques in which the confusion matrix is computed. Moreover, validation is subject to the 3 scenarios such as NIRS, EEG & Hybrid. The combination of EEG-NIRS will always yield the best ever results compared to the solitary BCI application. On the other hand, the performance of the BCI system is computed in 6 metrics they are,

    • Kappa Co-efficient
    • ROC Curve
    • Computation Time
    • Sensitivity
    • Specificity / Exactness
    • Accuracy / Precision

So far, we have brainstormed in the fields of mental stress detection based on hybrid datasets. Besides, we hope that you understand the concepts accompanied by this project idea. If you still have any doubts about the proposed area, you are suggested to reach out to technicians at any time, we are there to help you out. On the other hand, it is very important to note the future directions of the BCI systems. 

Future Directions of Brain computer interface

  • BCI System’s Consistency in Massive User Populations
  • Enhanced Signal Acquisition Devices
  • Brain computer interface Distribution & Authentication

The itemized above are some of the future directions of the BCI system. Apart from this, there are so many interesting futuristic directions are exist. Do you are really eager to know about further information you can have interactions with our technical crew? In the end, we have debated the major areas of brain computer interface projects. In addition, we are also expecting you guys to reconnoiter furthermore in the same area.

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