Brain Computer Interface for Emergency Virtual Voice

Brain computer interface (BCI) is one of the thriving emergent technology which acts as an interface between a brain and an external device. BCI for speech communication is acquiring recognition in various fields. Speech is one of the most natural ways to express thoughts and feelings by articulate vocal sounds. The purpose of this study is to restore communication ability of the people suffering from severe muscular disorders like amyotrophic lateral sclerosis (ALS), stroke which causes paralysis, locked-in syndrome, tetraplegia and Myasthenia gravis. They cannot interact with their environment even though their intellectual capabilities are intact. Our work attempts to provide summary of the research articles being published in reputed journals which lead to the investigation of published BCI articles, BCI prototypes, Bio-Signals for BCI, intent of the articles, target applications, classification techniques, algorithms and methodologies, BCI system types. Thus, the result of detailed survey presents an outline of available studies, recent results and looks forward to future developments which provides a communication pathway for paralyzed patients to convey their needs

Speech Processes for Brain-Computer Interfaces

Speech interfaces have become widely used and are integrated in many applications and devices. However, speech interfaces require the user to produce intelligible speech, which might be hindered by loud environments, concern to bother bystanders or the general in- ability to produce speech due to disabilities. Decoding a usera s imagined speech instead of actual speech would solve this problem. Such a Brain-Computer Interface (BCI) based on imagined speech would enable fast and natural communication without the need to actually speak out loud. These interfaces could provide a voice to otherwise mute people. This dissertation investigates BCIs based on speech processes using functional Near In- frared Spectroscopy (fNIRS) and Electrocorticography (ECoG), two brain activity imaging modalities on opposing ends of an invasiveness scale. Brain activity data have low signal- to-noise ratio and complex spatio-temporal and spectral coherence. To analyze these data, techniques from the areas of machine learning, neuroscience and Automatic Speech Recog- nition are combined in this dissertation to facilitate robust classification of detailed speech processes while simultaneously illustrating the underlying neural processes. fNIRS is an imaging modality based on cerebral blood flow. It only requires affordable hardware and can be set up within minutes in a day-to-day environment. Therefore, it is ideally suited for convenient user interfaces. However, the hemodynamic processes measured by fNIRS are slow in nature and the technology therefore offers poor temporal resolution. We investigate speech in fNIRS and demonstrate classification of speech processes for BCIs based on fNIRS. ECoG provides ideal signal properties by invasively measuring electrical potentials artifact- free directly on the brain surface. High spatial resolution and temporal resolution down to millisecond sampling provide localized information with accurate enough timing to capture the fast process underlying speech production. This dissertation presents the Brain-to- Text system, which harnesses automatic speech recognition technology to decode a textual representation of continuous speech from ECoG. This could allow to compose messages or to issue commands through a BCI. While the decoding of a textual representation is unparalleled for device control and typing, direct communication is even more natural if the full expressive power of speech - including emphasis and prosody - could be provided. For this purpose, a second system is presented, which directly synthesizes neural signals into audible speech, which could enable conversation with friends and family through a BCI. Up to now, both systems, the Brain-to-Text and synthesis system are operating on audibly produced speech. To bridge the gap to the final frontier of neural prostheses based on imagined speech processes, we investigate the differences between audibly produced and imagined speech and present first results towards BCI from imagined speech processes. This dissertation demonstrates the usage of speech processes as a paradigm for BCI for the first time. Speech processes offer a fast and natural interaction paradigm which will help patients and healthy users alike to communicate with computers and with friends and family efficiently through BCIs

Near-Infrared Spectroscopy for Brain Computer Interfacing

A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user and may even lead to abandonment of the device. A more suitable and usable interface is needed to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. This work implements a novel approach to BCI design, using noninvasive near-infrared spectroscopic (NIRS) techniques to develop a userfriendly optical BCI. NIRS is a practical non-invasive optical technique that can detect characteristic haemodynamic responses relating to neural activity. This thesis describes the use of NIRS to develop an accessible BCI system requiring very little user training. In harnessing the optical signal for BCI control an assessment of NIRS signal characteristics is carried out and detectable physiological effects are identified for BCI development. The investigations into various mental tasks for controlling the BCI show that motor imagery functions can be detected using NIRS. The optical BCI (OBCI) system operates in realtime characterising the occurrence of motor imagery functions, allowing users to control a switch - a “Mindswitch”. This work demonstrates the great potential of optical imaging methods for BCI development and brings to light an innovative approach to this field of research

Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

Toward an Imagined Speech-Based Brain Computer Interface Using EEG Signals

Individuals with physical disabilities face difficulties in communication. A number of neuromuscular impairments could limit people from using available communication aids, because such aids require some degree of muscle movement. This makes brain–computer interfaces (BCIs) a potentially promising alternative communication technology for these people. Electroencephalographic (EEG) signals are commonly used in BCI systems to capture non-invasively the neural representations of intended, internal and imagined activities that are not physically or verbally evident. Examples include motor and speech imagery activities. Since 2006, researchers have become increasingly interested in classifying different types of imagined speech from EEG signals. However, the field still has a limited understanding of several issues, including experiment design, stimulus type, training, calibration and the examined features. The main aim of the research in this thesis is to advance automatic recognition of imagined speech using EEG signals by addressing a variety of issues that have not been solved in previous studies. These include (1)improving the discrimination between imagined speech versus non-speech tasks, (2) examining temporal parameters to optimise the recognition of imagined words and (3) providing a new feature extraction framework for improving EEG-based imagined speech recognition by considering temporal information after reducing within-session temporal non-stationarities. For the discrimination of speech versus non-speech, EEG data was collected during the imagination of randomly presented and semantically varying words. The non-speech tasks involved attention to visual stimuli and resting. Time-domain and spatio-spectral features were examined in different time intervals. Above-chance-level classification accuracies were achieved for each word and for groups of words compared to the non-speech tasks. To classify imagined words, EEG data related to the imagination of five words was collected. In addition to words classification, the impacts of experimental parameters on classification accuracy were examined. The optimization of these parameters is important to improve the rate and speed of recognizing unspoken speech in on-line applications. These parameters included using different training sizes, classification algorithms, feature extraction in different time intervals and the use of imagination time length as classification feature. Our extensive results showed that Random Forest classifier with features extracted using Discrete Wavelet Transform from 4 seconds fixed time frame EEG yielded that highest average classification of 87.93% in classification of five imagined words. To minimise within class temporal variations, a novel feature extraction framework based on dynamic time warping (DTW) was developed. Using linear discriminant analysis as the classifier, the proposed framework yielded an average 72.02% accuracy in the classification of imagined speech versus silence and 52.5% accuracy in the classification of five words. These results significantly outperformed a baseline configuration of state-of-the art time-domain features