Tongue-Operated Biosignal over EEG and Processing with Decision Tree and kNN

Abstract

Tongue-machine interface (TMI) is a feasible way between the assistive technologies and paralyzed individuals who have lost their abilities to communicate with the environment. Researchers have presented equipment based tongue-machine interfaces to reach a reliable and speedy system. However, this kind of interfaces may occur a way of obtrusive, unattractive and unhygienic for disabled persons. In this research, we intended to propose a natural, unobtrusive and robust glossokinetic potential signals (GKP) based TMI exploring the success of the novel machine learning algorithms. The tongue is bound up with cranial nerves to the brain, which can escape from the spinal cord injuries in general. Moreover, the tongue has highly capable of sophisticated manipulation tasks with less perceived exertion in the oral cavity and gives degrees of privacy. In this study, ten naive healthy subjects have attended who were between 22-34 ages. Decision Tree (DT) and k-Nearest Neighbors (kNN) algorithms were used with Mean-Absolute Value (MAV) and Power Spectral Density (PSD) methods. Moreover, Discrete Wavelet Transform (DWT) was implemented to reveal the theta and delta subbands. In the study, the highest value was provided as 96.77% by the k-Nearest Neighbor algorithm for the best participant. Furthermore, the GKP-based TMI may be an alternative system for the limitations of the brain-computer interfaces. It is well-known that EEG deficits are major concerns for brain-computer interfaces.

Feature and Classification Analysis for Detection and Classification of Tongue Movements From Single-Trial Pre-Movement EEG

“…6. Grand average topological plots of the Alpha [8][9][10][11][12][13] Hz and Beta [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz bandpower at movement onset, for the four different movement types averaged across all subjects and all trials. The power was calculated as meansquare of the bandpass filtered signal, referenced to common average reference, from 0.5 seconds before until movement onset.…”

“…This is a challenge to provide individuals with severe disabilities. Individuals with tetraplegia who still have motor functionality above the neck, such as individuals with SCI or early spinal-onset ALS, can use some control options such as lip-sip-suck control [7], [8], eye-tracking [9]- [11], or electrooculography (EOG) or facial electromyography (EMG) [12], or tongue control [13]- [20]. However, if the cranial nerves are also affected the individual may be in a locked-in state (LIS) and can then only use a brain-computer interface (BCI) as a control option.…”

“…Furthermore, the stimuli might cause fatigue and/or require eye-gaze which some LIS patients do not have. As an alternative to stimuli-based BCI, Nam et al utilized the glossokinetic potentials (GKP), which are elicited when the tip of the tongue makes contact with tissue inside the mouth [13], [18]- [20], to successfully control a robotic wheelchair through EEG measurements [18]. While it showed a new control option, it requires physical tongue movements similar to other proposed tongue control interfaces that achieve Aalborg University, 9220 Aalborg, Denmark (e-mail: rlk@hst.aau.dk; naja@hst.aau.dk).…”

“…Still, tongue movement types and classification of such have not yet been studied. Until now tongue movements have been treated as undesirable artifacts [36], or a single muscle group (class) [30], or with a focus on GKP [20]. In a feasibility study, we showed the possibility of classifying different tonguemovement types (left, right, up, and down movement) using only EEG signals generated before the actual movement [37].…”

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

“…The acoustic sound stimulates the auditory cortex in the brain which generates electrical signals that can be detected by the electrodes or small metal plates attached to the scalp. Different research studies have proposed to analyze EEG signals and extract the relationship between brain signals and speech behaviour [ 56 , 57 , 58 , 59 ]. Although EEG can provide information about the speech patterns, the nature of the EEG signal is complex and susceptible to noise, which makes the part of the EEG complex signal relating to the auditory system difficult to be separated from other electrical activities of the brain [ 8 , 60 ].…”