Using Brain Computer Interface for Synthesized Speech Communication for the Physically Disabled

Soman, Sumit; Murthy, B. K.

doi:10.1016/j.procs.2015.02.023

Cited by 32 publications

(7 citation statements)

References 20 publications

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“…Soman et al [105] designed and implemented a BCI-based system for synthesized speech generation that is based on the user's EEG signals. This type of system is especially beneficial for patients with locomotive disorders like lockedin disease, who can share information with their caregivers via this functionality.…”

Section: Rnnmentioning

confidence: 99%

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

et al. 2023

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Epilepsy is a neurological condition affecting around 50 million individuals worldwide, reported by the World Health Organization. This is identified as a hypersensitive disease by clinical associations. The unique characteristics of Electroencephalography have proven to be stable and universal; therefore, researchers have a lot of credibilities. So, it is the most used test for Epileptic Seizure detection and prediction. This study examines the contributions that have so far been made utilizing Electroencephalography technology to detect, predict, and monitor Epileptic Seizures. We have reviewed around 56 research articles, and those papers are selected from different academic databases. The studies explored various approaches, including Machine Learning, Deep Learning, and the Internet of Things framework. A comprehensive discussion of different classification algorithms is analyzed, and their performances are explored. Furthermore, various open issues of the stated approach are discussed, and potential future works are addressed.

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Section: Rnnmentioning

confidence: 99%

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

et al. 2023

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“…The acquisition of neural signals, along with stimulation and/or neuromodulation using BCIs [ 3 ], aims to alleviate some of these conditions. In addition, neural signals have been utilized in various fields such as security and privacy, cognitive training, imaginary or silent speech recognition [ 4 , 5 ], emotion recognition [ 6 , 7 ], mental state recognition [ 8 ], human identification [ 9 , 10 ], speech communication [ 11 ], synthesized speech communication [ 12 ] gaming [ 13 ], Internet of Things (IoT) applications [ 14 ], Brain Machine Interface (BMI) applications [ 15 , 16 , 17 ], neuroscience research [ 18 , 19 ], speech activity detection [ 20 , 21 ] and more. The first step involves collecting neural signals from patients, which are then processed and analyzed.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Sen,

Sheehan,

Raman

et al. 2023

Sensors

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Brain–Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.

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“…This system enables patients to communicate by selecting the desired options from a configured list by performing eyeblinks. It is stated that the system gives an offline accuracy of 95%, average across users [12]. Akram et al, designed a P300-BCI spelling system, which they call the T9 interface.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Eye-Blink to Speech Conversion by Dynamic Time Warping

Ekim¹,

Atasoy²,

IKIZLER³

2021

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Motor neuron patients such as paralysis, locking syndrome, and amyotrophic lateral sclerosis can see and hear what is happening in their environment, but cannot communicate with their environment. It is very important for these patients, who do not have any physical function other than eye movements, to be able to express their needs, feelings and thoughts. Therefore, to express the thoughts, needs and feelings of these patients, a system that converts eye-blink signals to speech was developed in this study. The main purpose of the designed system is high accuracy, low cost, high speed and independence from environmental factors. Undoubtedly, it is also very important that it causes as little discomfort to the patient as possible. Morse-coded signals generated by voluntary eye-blinks and the single-channel wireless NeuroSky MindWave Mobile device eliminates the need for cost-increasing equipment such as a camera or eye tracker and environmental factors such as light. With the use of Dynamic Time Warping (DTW), an algorithm which works at high speed and high accuracy at the time domain and does not require any training process has been implemented. In this way, the recorded speech was performed with a quite impressive accuracy.

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Using Brain Computer Interface for Synthesized Speech Communication for the Physically Disabled

Cited by 32 publications

References 20 publications

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

A New Approach for Eye-Blink to Speech Conversion by Dynamic Time Warping

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