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Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface
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In: Sensors; Volume 22; Issue 2; Pages: 535 (2022)
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Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible
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In: NeuroSci; Volume 3; Issue 2; Pages: 226-244 (2022)
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The perspectives of augmentative and alternative communication experts on the clinical integration of non-invasive brain-computer interfaces
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In: Special Education and Communication Disorders Faculty Publications (2022)
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Silent EEG-Speech Recognition Using Convolutional and Recurrent Neural Network with 85% Accuracy of 9 Words Classification
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In: Sensors ; Volume 21 ; Issue 20 (2021)
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Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
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In: Algorithms ; Volume 14 ; Issue 10 (2021)
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Monitoring the Cortical Activity of Children and Adults during Cognitive Task Completion
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In: Sensors ; Volume 21 ; Issue 18 (2021)
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Evolution of human computer interaction
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In: Sci. Visualization ; Scientific Visualization (2021)
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NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals
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In: Sensors ; Volume 20 ; Issue 8 (2020)
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Interpreting Speech and Sounds from Neural Activity, a Brief Overview
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Abstract:
For people who are mute, or are completely paralyzed, one of the primary problems they have to deal with is being able to communicate. One potential solution to compensate for decreased communication functions is by using a brain-computer interface (BCI). The idea would be to quantify neural activation in the brain that correlates to imagined speech from the patient, and decode that into legible text that can be interpreted by the receiver. Due to the intricacy of speech interpretation, direct access to regions of the brain and individual neurons isrequired. As a result, many tests done on BCI speech interpretation involve using ECoG sensors on epilepsy patients when they are available. Some approaches used to analyze these signals for feature extraction include word based classification, and phoneme based classification. One approach mentioned less in the literature, is if there is a method to pull a sound signal directly from the activated regions of the brain. Advancement of the technology has potential use as a speech replacement for people suffering from paralysis, as well as in prosthetics.
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Keyword:
brain-computer interface; neural engineering
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URL: http://hdl.handle.net/1920/11764
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Evaluating Person-Centered Factors Associated with Brain-Computer Interface Access to a Commercial Augmentative and Alternative Communication Device
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Real-time decoding of perceived and produced speech from human cortical activity
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In: Moses, David Aaron. (2018). Real-time decoding of perceived and produced speech from human cortical activity. UC San Francisco: Bioengineering. Retrieved from: http://www.escholarship.org/uc/item/3sn3080p (2018)
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Real-time decoding of perceived and produced speech from human cortical activity
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A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence.
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Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface
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In: Sensors ; Volume 18 ; Issue 9 (2018)
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On-Talk and Off-Talk Detection: A Discrete Wavelet Transform Analysis of Electroencephalogram ; 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018)
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Toward a brain-computer interface for speech restoration ; Vers une interface cerveau-machine pour la restauration de la parole
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In: https://tel.archives-ouvertes.fr/tel-01693270 ; Electronics. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAS008⟩ (2017)
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Excuse Me, Do I Know You From Somewhere? Unaware Facial Recognition Using Brain-Computer Interfaces
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Surface electromyographic control of a novel phonemic interface for speech synthesis
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Rehabilitation of face-processing skills in an adolescent with prosopagnosia: Evaluation of an online perceptual training programme.
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