Brain-Computer Interfaces for Speech Communication

This paper briefly reviews current silent speech methodologies for normal and disabled individuals. Current techniques utilizing electromyographic (EMG) recordings of vocal tract movements are useful for physically healthy individuals but fail for tetraplegic individuals who do not have accurate voluntary control over the speech articulators. Alternative methods utilizing EMG from other body parts (e.g., hand, arm, or facial muscles) or electroencephalography (EEG) can provide capable silent communication to severely paralyzed users, though current interfaces are extremely slow relative to normal conversation rates and require constant attention to a computer screen that provides visual feedback and/or cueing. We present a novel approach to the problem of silent speech via an intracortical microelectrode brain computer interface (BCI) to predict intended speech information directly from the activity of neurons involved in speech production. The predicted speech is synthesized and acoustically fed back to the user with a delay under 50 ms. We demonstrate that the Neurotrophic Electrode used in the BCI is capable of providing useful neural recordings for over 4 years, a necessary property for BCIs that need to remain viable over the lifespan of the user. Other design considerations include neural decoding techniques based on previous research involving BCIs for computer cursor or robotic arm control via prediction of intended movement kinematics from motor cortical signals in monkeys and humans. Initial results from a study of continuous speech production with instantaneous acoustic feedback show the BCI user was able to improve his control over an artificial speech synthesizer both within and across recording sessions. The success of this initial trial validates the potential of the intracortical microelectrode-based approach for providing a speech prosthesis that can allow much more rapid communication rates

A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced

Development of speech prostheses: current status and recent advances

This is an Accepted Manuscript of an article published by Taylor & Francis in Expert Review of Medical Devices on September, 2010, available online: http://www.tandfonline.com/10.1586/erd.10.34.Brain–computer interfaces (BCIs) have been developed over the past decade to restore communication to persons with severe paralysis. In the most severe cases of paralysis, known as locked-in syndrome, patients retain cognition and sensation, but are capable of only slight voluntary eye movements. For these patients, no standard communication method is available, although some can use BCIs to communicate by selecting letters or words on a computer. Recent research has sought to improve on existing techniques by using BCIs to create a direct prediction of speech utterances rather than to simply control a spelling device. Such methods are the first steps towards speech prostheses as they are intended to entirely replace the vocal apparatus of paralyzed users. This article outlines many well known methods for restoration of communication by BCI and illustrates the difference between spelling devices and direct speech prediction or speech prosthesis

Brain-Computer Interfacing Embedded in Intelligent and Affective Systems

In this talk we survey recent research views on non-traditional brain-computer interfaces (BCI). That is, interfaces that can process brain activity input, but that are designed for the ‘general population’, rather than for clinical purposes. Control of applications can be made more robust by fusing brain activity information with other information, either explicitly provided by a user (such as commands) or extracted from the user by interpreting his or her behavior (movements, posture, gaze, facial xpression, nonverbal speech) or sensing (neuro-)physiological characteristics (skin conductivity, heart rate, brain activity). Traditional BCI research, guided by clinical applications and focused on patients that have no other means than their brain activity for control and communication have neglected a possible role for BCI in multimodal interaction with intelligent devices. We will emphasize such a role and will also look at some roadmaps for BCI research (2020 and beyond) and how their authors see the development of BCI for intelligent systems

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

Direct Classification of All American English Phonemes Using Signals From Functional Speech Motor Cortex

Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, we investigated words that span the entire set of phonemes in the General American accent using ECoG with 4 subjects. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits/s (33.6 words min), supporting pursuit of speech articulation for BCI control

Language Model Applications to Spelling with Brain-Computer Interfaces

Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models appli

Brain-Driven Representation Learning Based on Diffusion Model

Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. Denoising diffusion probabilistic models (DDPMs), which have recently gained prominence in diverse areas for their capabilities in representation learning, are explored in our research as a means to address this issue. Using DDPMs in conjunction with a conditional autoencoder, our new approach considerably outperforms traditional machine learning algorithms and established baseline models in accuracy. Our results highlight the potential of DDPMs as a sophisticated computational method for the analysis of speech-related EEG signals. This could lead to significant advances in brain-computer interfaces tailored for spoken communication

Surface electromyographic control of a novel phonemic interface for speech synthesis

Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of eight training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.F31 DC014872 - NIDCD NIH HHS; R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; T90 DA032484 - NIDA NIH HHShttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesishttps://www.ncbi.nlm.nih.gov/pubmed/?term=Surface+electromyographic+control+of+a+novel+phonemic+interface+for+speech+synthesisPublished versio