ROLE OF EEG IN BRAIN TO COMPUTER INTERFACE

Brain has always been a mystery for us. We have reached moon, Everest and where not, but brain is yet to be understood completely.  We invented machines, robots to make our life simpler, but it would become simpler if we could do all those tasks just by thinking and transmitting signals from one brain to another with the help of signal processing!! That day is not far away, thanks to BCI (Brain Computer interface).

BCI is direct communication pathway between brain and an external device.  In 1924, Hans Berger was first to discover electric activity in human brain that gave an edge to the signal processing to enter into the biology.  After analyzing the interrelation between EEG’s with brain diseases, a new possibility of research of human brain through EEG opened up.

Some of the advantages EEG Signals has in BCI are that it’s non-invasive, portable and cost effective. One need not cut open the brain. Rhythmic activity in brain is influenced by level of alertness and mental state which is exploited while measuring EEG’s. Since signals are read from the surface, it also makes it prone to noise and results in poor signal resolution, blur and dispersion of the electromagnetic waves created by neurons. Still the cost and the fact that artifacts can be removed, make it top choice for BCI. However, recently using advanced functional Magnetic Resonance Imaging (fMRI) and EEG, control of the flight of virtual helicopter was demonstrated using EEG’s.

BCI is a promising technology for people suffering from paralyses. It can be used to restore mobility in paralyzed limbs. By 2000, researchers had created a thought-translation device for completely paralyzed patients which allowed patients to select characters based on their thoughts. By 2008, researchers from Belgium, Spain and Switzerland created BCI that controlled a motorized wheel chair with high accuracy but it was not perfect.

Neuro gaming is another field where using BCI, a player interacts with the console without using controller. Music and scenery is adjusted according to the mood of player judge by his EEG, heart rate and cognitive state. This allows game play to be more realistic.

Undoubtedly, BCI-EEG is a prominent technology in the signal processing domain and will bring transformation in the field of medicine and our lives.

Computer Vision: Current Trends and Future Possibilities

Extracting some useful information from images is considered as Computer Vision. These images can be of any form from Visual to Infrared to X-rays in the whole electromagnetic spectrum. The basic idea is to duplicate human visual perception in images to extract the same information.

Some major applications of Computer vision consists tasks including Object detection, Object tracking, segmentation, Image inpainting and 3d modelling from images.

A lot of research work is carried out all over the globe in all of the above mentioned fields. So in this article we are going to discuss some of the very interesting yet strange fields for new comers.

Image Inpainting:

Image inpainting is a process to recover some useful information from deteriorated images or give some artistic look to images by removing unwanted objects still maintaining smooth background. Such a task is a daily part of human life as imagine a person at some distinct place or removing a particular object from a scene but in computers, this is trivial. 

Method:

First a binary map is created on the basis of which part of the image is to be removed. Now that part of the image is filled in a manner to minimize energy. This is often done using a very simple operator called Laplacian operator. This is essentially second order derivative of image. Second order derivatives are used because its direction is similar to the direction of edges rather than perpendicular to it as in the case of first order derivative. Thus we find to minimize this function as this would perfectly reflect the second order derivative.

Image Segmentation:

Segmentation is a process of image processing to segment out one or more objects from an image. The goal is to find out a boundary of pixels that can perfectly differentiate between two objects based on colour or shape or both. Applications for image segmentation includes Object detection, Face detection and in medical imaging. Tumour detection, Surgery planning and diagnosis of anatomical structures are some of the major applications of segmentation in medical imaging.

Motion Analysis:

Motion analysis is in the simplest case to find out a moving object from a sequence of images. This work can be extended to find the direction of movement, velocity and displacement calculation and object tracking. The basic idea is find out a static region (background) and a moving region with substantial displacement. One very popular method for this is finding the Optical flow. Motion analysis is extremely important in Surveillance and video object tracking. Tracking with a moving camera increases the complexity a lot due to the relative motion between camera and the object. Tracking with multiple cameras with overlapping or non-overlapping regions are current research issues in Object tracking.

CNN Behavior Analysis: Visualizing the activations and first-layer weights

Layer Activations 

 

The most straight-forward visualization technique is to show the activations of the network during the forward pass. For ReLU networks, the activations usually start out looking relatively blobby and dense, but as the training progresses the activations usually become more sparse and localized. One dangerous pitfall that can be easily noticed with this visualization is that some activation maps may be all zero for many different inputs, which can indicate dead filters, and can be a symptom of high learning rates.

Convolutional/FC Filters

 

The second common strategy is to visualize the weights. These are usually most interpretable on the first CONV layer which is looking directly at the raw pixel data, but it is possible to also show the filter weights deeper in the network. The weights are useful to visualize because well-trained networks usually display nice and smooth filters without any noisy patterns. Noisy patterns can be an indicator of a network that hasn't been trained for long enough, or possibly a very low regularization strength that may have led to over fitting.

Back propagation 

 

Primary reason we are interested in this problem is that in the specific case of Neural Networks, f will correspond to the loss function ( L ) and the inputs x will consist of the training data and the neural network weights. Training data is given and fixed so it is a constant factor in the equation. So here we are left with two variables which are weights and biases of each layer. In back propagation Convolutional Neural Network compute the gradient at every layer according to the loss function at output and these calculated new weights are then updated to converse the network for the final solution.

What is an Image and Image Processing?

IMAGE:

An image is a still representation of any worldly physical thing,be it a person or an object.It  depicts our  visual perception.In Digital representation an image is a rectangular grid of pixels.An image,therefore,has a definite height and width measured in pixels.

Image may be 2-D (a photograph) or 3-D (statue or hologram). It may be volatile (like the one formed in front of a mirror) or fixed (like the one recorded on a textile).

PIXELS:

Pixels are the smallest unit of an image or the building blocks of an image,similar to the cells that make up a human body.

A Pixel is a square shaped structure.Each pixel has some color intensity. A pixel conveys information about the image in the form of the colour intensities that it consists of. The colors talked about here are the primary colors: RED,GREEN,BLUE. All other colors are formed from these colors only.

Color intensities in a pixel are  represented in the form of bits.Each of the three RGB colors have intensities in the range 0-255, i.e., 8 bits are used for each color in a single pixel. In addition to these (8*3=) 24 bits, 8 bits are used for transparency in an image.

The higher the number between 0 and 255,the more will be the intensity of the color and the brighter the color will be.

REPRESENTATION OF AN IMAGE:

An image is represented in the form of pixel values. Suppose an image has 1024 pixels row wise and 1024 pixels column wise, then the size of that image will be 1024x1024, and total number of pixels will be (1024*1024=) 1048576. 

TYPES OF IMAGES:

Colored Image: An image having matrices for all the three colors .

Monochrome Image: An image having only one color.

Black and White Image: An image having matrix of only binary values i.e.,0 and 0 showing black color and 1 showing white.

Gray scale Image: An image having shades of gray only (neither complete black nor complete white). The new matrix in this type of image is formed by taking the average of the three color intensities.

 

IMAGE PROCESSING

Image Processing includes different processes that can be performed on an image in order to enhance the quality of an image. These processes can be:

1.Removal of noise.

2.Extraction of only text from the image.

3.Changing the intensity of an image.

4.Adjusting the contrast of an image.