Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution

Tzimourta, Katerina D.; Tsoulos, Ioannis G.; Bilero, Thanasis; Tzallas, Alexandros T.; Tsipouras, Markos G.; Γιαννακέας, Νικόλαος

doi:10.3390/inventions3030051

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…Additionally, a sub-band morphological operation method has also been used successfully to detect cerebral aneurysms [56] and convolutional neural networks have been employed for the classification of leukocytes categories and leukemia prediction [57]. Furthermore, wearable electroencephalogram (EEG) recorders and Brain Computer Interface software have been proposed to aid in the assessment of alcohol-related brain waves [58]. More specifically, calculated spectral and statistical properties were used for classification, and Grammatical Evolution was applied.…”

Section: Introductionmentioning

confidence: 99%

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Toki

Tatsis

et al. 2023

Signals

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Early detection and evaluation of children at risk of neurodevelopmental disorders and/or communication deficits is critical. While the current literature indicates a high prevalence of neurodevelopmental disorders, many children remain undiagnosed, resulting in missed opportunities for effective interventions that could have had a greater impact if administered earlier. Clinicians face a variety of complications during neurodevelopmental disorders’ evaluation procedures and must elevate their use of digital tools to aid in early detection efficiently. Artificial intelligence enables novelty in taking decisions, classification, and diagnosis. The current research investigates the efficacy of various machine learning approaches on the biometric SmartSpeech datasets. These datasets come from a new innovative system that includes a serious game which gathers children’s responses to specifically designed speech and language activities and their manifestations, intending to assist during the clinical evaluation of neurodevelopmental disorders. The machine learning approaches were used by utilizing the algorithms Radial Basis Function, Neural Network, Deep Learning Neural Networks, and a variation of Grammatical Evolution (GenClass). The most significant results show improved accuracy (%) when using the eye tracking dataset; more specifically: (i) for the class Disorder with GenClass (92.83%), (ii) for the class Autism Spectrum Disorders with Deep Learning Neural Networks layer 4 (86.33%), (iii) for the class Attention Deficit Hyperactivity Disorder with Deep Learning Neural Networks layer 4 (87.44%), (iv) for the class Intellectual Disability with GenClass (86.93%), (v) for the class Specific Learning Disorder with GenClass (88.88%), and (vi) for the class Communication Disorders with GenClass (88.70%). Overall, the results indicated GenClass to be nearly the top competitor, opening up additional probes for future studies toward automatically classifying and assisting clinical assessments for children with neurodevelopmental disorders.

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Section: Introductionmentioning

confidence: 99%

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Toki

Tatsis

et al. 2023

Signals

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“…Only performing feature analysis from a single aspect will cause the loss of effective information. Tzimourta et al extracted six time domain features and five spectral features for analysis and found that the classification results using two fusion features are more ideal and the best accuracy reached 85.5% [ 17 ]. Hu, J. et al extracted four characteristics of sample entropy, fuzzy entropy, approximate entropy, and power spectrum entropy, and fused together as an input to the gradient-enhanced decision tree to determine whether the driver was in a fatigue state.…”

Section: Introductionmentioning

confidence: 99%

Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

Liu

Yang

Chen

et al. 2021

Entropy

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With the rapid development of modern social science and technology, the pace of life is getting faster, and brain fatigue has become a sub-health state that seriously affects the normal life of people. Electroencephalogram (EEG) signals reflect changes in the central nervous system. Using EEG signals to assess mental fatigue is a research hotspot in related fields. Most existing fatigue detection methods are time-consuming or don’t achieve satisfactory results due to insufficient features extracted from EEG signals. In this paper, a 2-back task is designed to induce fatigue. The weight value of each channel under a single feature is calculated by ReliefF algorithm. The classification accuracy of each channel under the corresponding features is analyzed. The classification accuracy of each single channel is combined to perform weighted summation to obtain the weight value of each channel. The first half channels sorted in descending order based on the weight value is chosen as the common channels. Multi-features in frequency and time domains are extracted from the common channel data, and the sparse representation method is used to perform feature fusion to obtain sparse fused features. Finally, the SRDA classifier is used to detect the fatigue state. Experimental results show that the proposed methods in our work effectively reduce the number of channels for computation and also improve the mental fatigue detection accuracy.

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“…EEG sensors are wearable [ 6 ] non-invasive, portable and mobile [ 7 ], with excellent temporal resolution, and acceptable spatial resolution [ 8 ]. This humble diagnosis device is been transformed into currently the best approach to detect, out-of-the lab in an ambulatory context, information from the Central Nervous System and to use that information to volitionally drive cars, steer drones, write emails, control wheelchairs or to assess alcohol consumption [ 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018

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The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution

Cited by 12 publications

References 28 publications

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

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