Effective Brain Controlled Interface for Wheelchair: An Organized Review

A direct line of communication between humans and machines is provided by a brain-computer-interface (BCI). Over past few decades, it has been successfully applied in numerous ways. These days, Machine_Learning and Deep_Learning approach are heavily used in a wide range of sectors, from biology to economics. Generally speaking, there are two methods to use these strategies: either build unique designs or modify well-known models along with architectures to fit the available data. This study presents, for the very first duration, a This review article examines the implementation of Machine Learning (ML) technological advances in the BCI. It looks at the several types of study in this field and investigates the role that machine learning plays in carrying out different BCI activities. These comprise the detection of mental states, the classification of tasks, the classification of emotional states, the classification for electroencephalogram signals as well as the presence for event-related potentials (ERP), and the classification of motor imagery. This study looks into the extraction of features, selection, and classifications and offers a comparative evaluation of the methodologies that have been studied. This article discusses developments in BCI-ML and also the kinds of innovations that be required eventually to create exceeding version of BCI utilization.