Gradjo Labs - R&D in Brain Signal Analysis
Ravichandra Reddy V.
Entrepreneurial Founder leading sustainable coffee and cultural innovation.
We are very delighted to let you know that we at GRADJO provides GRADJO labs where student can innovate and can make the things that till aren’t available in the market and these products will be the future. Similarly, we are now starting our work on BRAIN SIGNAL ANALYSIS. We are looking forward to model a product with Brain Signal analysis extended with Artificial Intelligence (AI). Scientists across the globe are researching on this topic, we too have machines like EEG and EMG which does these works and provide a sturdy influence to the medical science. We at GRADJO will be providing the same environment to the students who will register with us so that they can develop their knowledge in this field and can explore the true meaning of science and technology.
For Understanding BSA, there are another things related that are needed to be clear, those are:
- Artificial Intelligence:
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
- Knowledge
- Reasoning
- Problem solving
- Perception
- Learning
- Planning
- Ability to manipulate and move objects
Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.
Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition. The training of the machine is called the machine learning where the machine is meant to learn about the circumstances or the environment to generate certain results.
For BSA we need an AI system which will let the machine understand these signal to provide the desired output, thus we use ANN (Artificial neural Networks).
- ANN (Artificial Neural Network):
Artificial Neural Network (ANN) is a system or a type of network which is a replica of our neural system. The way in which neural system of living beings works, ANN works exactly the same. It basically follows “Lavenberg-Marquandt Algorithm”.
Artificial neural networks (ANNs) have now been applied to a wide variety of real-world problems in many fields of application. The attractive and flexible characteristics of ANNs, such as their parallel operation, learning by example, associative memory, multifactorial optimization and extensibility, make them well suited to the analysis of biological and medical signals.
Artificial neural networks (ANNs) are computational framework inspired by our expanding knowledge of the activity of networks of biological neurons in the brain. ANNs cannot hope to reproduce all the still not well-understood complexities of actual brain networks. Rather, most ANNs are implemented as sets of nonlinear summing elements interconnected by weighted links, forming a highly simplified model of brain connectivity. The basic operation of such artificial neurons is to pass a weighted sum of their inputs through a nonlinear hard-limiting or soft “squashing” function. To form an ANN, these basic calculating elements (artificial neurons) are most often arranged in interconnected layers. Some neurons, usually those in the layer furthest from the input, are designated as output neurons. The initial weight values of the interconnections are usually assigned randomly.
The operation of most ANNs proceeds in two stages.
Rules used in the first stage, training (or learning), can be categorized as supervised, unsupervised, or reinforced.
Any AI system need training for its functioning, similarly ANN in an AI system which is firstly trained to acquire the knowledge of the data being provided to it, and according to those data it starts predicting the behavior of the system. Similarly ANN needed to be trained to gain desired results at the output.
The second stage is recall, in which the ANN generates output for the problem the ANN is designed to solve, based on new input data without (or sometimes with) further training signals.
2. ROLES OF ANNS IN BRAIN SIGNAL PROCESS
To date, ANNs have been applied to brain data for the following purposes:
a. Feature extraction, classification, and pattern recognition: ANNs here serve principally as non-linear classifiers. The inputs square measure preprocessed therefore on kind a feature area. ANNs square measure accustomed categories the collected knowledge into distinct categories. In different cases, inputs don't seem to be subjected to preprocessing however square measure given on to associate ANN to extract options of interest from the information.
b. Adaptive filtering and control: ANNs here operate inside control system systems to method dynamic inputs, adapting their weights “on the fly” to strain unwanted components of the input (adaptive filtering), or mapping their outputs to parameters utilized in on-line management (adaptive control).
c. Linear or nonlinear mapping: Here ANNs area unit accustomed remodel inputs to outputs of a desired kind. As an example, Associate in Nursing ANN would possibly remap its rectangular input file coordinates to circular or a lot of general coordinate systems.
d. Modeling: ANNs can be thought of as function generators that generate an output data series based on a learned function or data model. ANNs with two layers of trainable weights have been proven capable of approximating any nonlinear function.
e. Signal separation and DE convolution: These ANNs separate their input signals into the weighted sum or convolution of a number of underlying sources using assumptions about the nature of the sources or of their interrelationships (e.g., their independence).
f. Texture analysis and image segmentation: Image texture analysis is becoming increasingly important in image segmentation, recognition and understanding. Analysis 5 are being used to learn spatial or spatial-frequency texture features and, accordingly, to categorize images or to separate an image into sub images (image segmentation).
g. Edge detection: In an image, an edge or boundary between two objects can be mapped to a dark band between two lighter areas (objects). By using the properties of intensity discontinuity, ANNs can be trained to “recognize” these dark bands as edges, or can learn to "draw" such edges based on contrast and other information.
3. APPLICATION AREAS
In this section, we tend to illustrate applications of ANNs to brain signals through some examples
Involving biological science statistic and brain pictures. Biological science signals of clinical interest recorded noninvasively from humans embody encephalogram, MEG, and electromyogram information. Analysis in brain imaging includes the analysis of structural brain pictures, chiefly targeted on the extraction of three-D structural info, from numerous types of brain pictures (e.g., magnetic resonance pictures, MRI), yet as analysis of useful brain imaging series that chiefly reveal changes within the brain state throughout psychological feature tasks exploitation medical imaging techniques (e.g., fMRI and antilepton emission pictorial representation or PET). These examples, however, by no means that cowl all the publications within the field, whose range is growing apace.
In a same way GRADJO lab is providing a great opportunity for the research and technological student to enhance their knowledge in this field, GRADJO is also looking forward to make these product for the sake of the society, for its betterment and development.