Currently researchers have shown immeasurable awareness in Brain Computer Interface (BCI) systems, which enable any user to exchange intelligence and knowledge with surrounding and control instruments by using brain signals; concept is identified as Affective Computing. In this work we are using the SEED database, which is publically available to classify three emotions Positive, Negative and Neutral. Five electrode pairs from various brain regions like Prefrontal, Frontal, Temporal, Parietal and Occipital are selected for this work based on previous research. Diverse time domain and time frequency domain features are extracted from EEG signals. Wavelet Transform (WT) is used to extract a variety of time frequency domain features. Daubechies wavelet function (db6) with 6 levels of decomposition is used to split EEG signals into various frequency bands (δ, θ, α, β and γ). SVM and k-NN algorithms are used as classifiers to estimate classification performance. Hypothetical results illustrate an average classification accuracy of 62.4% for classifying three emotions. Gamma and Beta, the higher frequency bands perform well in emotion recognition.
Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. Moving something without touching. It has always something attractive for every person, speech recognition & head movement is being the common methods. In recent technology consider imagination of people. The main thing has to control peripheral by brain activities. Electrical activity of brain is magic. A brain (BCI) is a system which allows direct translation of brain state into action. A BCI system works by extracting user brain signals, applying machine learning algorithm to classify the users brain state &performing a computer controlled action.
Math plays a crucial role in each and every sector belonging to human beings in society. Sometimes, it is very difficult to recognize math equations and symbols due to variation in writing, change in stroke, touching symbols, and many more. The blind people get very low success in math recognition as compared to character and digit recognition. It is needed to develop blind math application in which various math equations and symbols have to be recognized. The proposed system uses a machine learning approach to identify the various math equations and symbols by extracting various statistical and complex features with well-known classifier viz. support vector machine, neural network, and K-nearest neighbor. The math documents have to be scanned and recognized. Finally, a text to speech converter has been made to get the contents of math documents for blind people in society. The proposed system will be helpful for blind math applications and it will not affect the health of blind people in terms of stress on the eye to recognize so the health can be maintained and documents can be read.The implemented system can be used by blind people to read the math documents, digits on a bank check, calculate the valuation of currency, etc.
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