Effective brain connectivity related to non-painful thermal stimuli using EEG.

Understanding the brain response to thermal stimuli is crucial in the sensory experience. This study focuses on non-painful thermal stimuli, which are sensations induced by temperature changes without causing discomfort. These stimuli are transmitted to the central nervous system through specific nerve fibers and are processed in various regions of the brain, including the insular cortex, the prefrontal cortex, and anterior cingulate cortex. Despite the prevalence of studies on painful stimuli, non-painful thermal stimuli have been less explored. This research aims to bridge this gap by investigating brain functional connectivity during the perception of non-painful warm and cold stimuli using electroencephalography (EEG) and the partial directed coherence technique (PDC). Our results demonstrate a clear contrast in the direction of information flow between warm and cold stimuli, particularly in the theta and alpha frequency bands, mainly in frontal and temporal regions. The use of PDC highlights the complexity of brain connectivity during these stimuli and reinforces the existence of different pathways in the brain to process different types of non-painful warm and cold stimuli.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks.

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Multi-channel and multi-harmonic analysis of Auditory Steady-State Response detection.

The Auditory Steady-State Response (ASSR) is a type of auditory evoked potential (AEP) generated in the auditory system that can be automatically detected by means of objective response detectors (ORDs). ASSRs are usually registered on the scalp using electroencephalography (EEG). ORD are univariate techniques, i.e. only uses one data channel. However, techniques involving more than one channel - multi-channel objective response detectors (MORDs) - have been showing higher detection rate (DR) when compared to ORD techniques. When ASSR is evoked by amplitude stimuli, the responses could be detected by analyzing the modulation frequencies and their harmonics. Despite this, ORD techniques are traditionally applied only in its first harmonic. This approach is known as one-sample test. The q-sample tests, however, considers harmonics beyond the first. Thus, this work proposes and evaluates the use of q-sample tests using a combination of multiple EEG channels and multiple harmonics of the stimulation frequencies and compare them with traditional one-sample tests. The database used consists of EEG channels from 24 volunteers with normal auditory threshold collected following a binaural stimulation protocol by amplitude modulated (AM) tone with modulating frequencies near 80 Hz. The best q-sample MORD result showed an increase in DR of 45.25% when compared with the best one-sample ORD test. Thus, it is recommended to use multiple channels and multiple harmonics, whenever available.

A "fractal" expander-compressor-supplier formative research method on array processing.

In the present research the typical triangle on formative research was extended to a double triangle for an overall career programme (here expander/ compressor) and funnel proposal was explored in a single course (as a "fractal" method). Array processing and ElectroEncephaloGram (EEG) techniques have been incorporated into a Digital Signal Processing (DSP) course and research projects. The present research question was: is it possible to insert array sensing on formative research in an undergraduate course of DSP? From over eight years, two semesters with different homework loads (homogeneous triangle vs expander-compressor-supplier distributions) were analysed in detail within the DSP evaluations and students chose between experimental applied analysis and a formative research project. Results showed that cognitive load was influenced positively in the expander-compressor-supplier distribution, showing that an increase of the efficiency undertook more undergraduate research on array processing and the decrease of the number of formative applied projects. Over a longer term (48 months) students undertook more undergraduate research works on array processing and DSP techniques. Supplementary Information: The online version contains supplementary material available at 10.1007/s10639-023-11837-y.

Dealing with correlations in the multichannel EEG using bipolar derivations and Monte Carlo simulations: application to the detection of auditory steady-state responses.

The multichannel objective response detection (MORD) techniques are statistical methods, which use information from more than one electroencephalography (EEG) channel, to infer the presence of evoked potential. However, the correlation level between the channels can lead to a decrease in MORD performance, such as an increase in the false positive (FP) rate and/or a decrease in the detection rate (DR). The present study aims to propose a method to deal with the correlations in the multichannel EEG. The method consists of making an adjustment in the Monte Carlo simulation, considering the information between channels. The MORD techniques with and without the new method were applied to an auditory steady-state response (ASSR) database, composed of the EEG multichannel of eleven volunteers during multifrequency stimulation. The proposed method kept the FP rate at values equal to or less than the significance level of the test and led to an increase of 8.51% in the DR in relation to non-application of the method. Results of this study indicate that the proposed method is an alternative to deal with the effect of the correlation between channels in situations where MORD techniques are applied.

Neuronal and behavioral affective perceptions of human and naturalness-reduced emotional prosodies.

Artificial voices are nowadays embedded into our daily lives with latest neural voices approaching human voice consistency (naturalness). Nevertheless, behavioral, and neuronal correlates of the perception of less naturalistic emotional prosodies are still misunderstood. In this study, we explored the acoustic tendencies that define naturalness from human to synthesized voices. Then, we created naturalness-reduced emotional utterances by acoustic editions of human voices. Finally, we used Event-Related Potentials (ERP) to assess the time dynamics of emotional integration when listening to both human and synthesized voices in a healthy adult sample. Additionally, listeners rated their perceptions for valence, arousal, discrete emotions, naturalness, and intelligibility. Synthesized voices were characterized by less lexical stress (i.e., reduced difference between stressed and unstressed syllables within words) as regards duration and median pitch modulations. Besides, spectral content was attenuated toward lower F2 and F3 frequencies and lower intensities for harmonics 1 and 4. Both psychometric and neuronal correlates were sensitive to naturalness reduction. (1) Naturalness and intelligibility ratings dropped with emotional utterances synthetization, (2) Discrete emotion recognition was impaired as naturalness declined, consistent with P200 and Late Positive Potentials (LPP) being less sensitive to emotional differentiation at lower naturalness, and (3) Relative P200 and LPP amplitudes between prosodies were modulated by synthetization. Nevertheless, (4) Valence and arousal perceptions were preserved at lower naturalness, (5) Valence (arousal) ratings correlated negatively (positively) with Higuchi's fractal dimension extracted on neuronal data under all naturalness perturbations, (6) Inter-Trial Phase Coherence (ITPC) and standard deviation measurements revealed high inter-individual heterogeneity for emotion perception that is still preserved as naturalness reduces. Notably, partial between-participant synchrony (low ITPC), along with high amplitude dispersion on ERPs at both early and late stages emphasized miscellaneous emotional responses among subjects. In this study, we highlighted for the first time both behavioral and neuronal basis of emotional perception under acoustic naturalness alterations. Partial dependencies between ecological relevance and emotion understanding outlined the modulation but not the annihilation of emotional integration by synthetization.

Hallazgos electroencefalográficos compatibles con encefalopatía en pacientes ingresados por covid-19 / Electroencephalographic findings compatible with encephalopathy in patients admitted for COVID-19

RESUMEN INTRODUCCIÓN: La covid-19 afecta principalmente al aparato respiratorio, sin embargo, también se ha descrito afectación tanto directa como indirecta en el sistema nervioso central y periférico, lo cual ocasiona una gran variedad de manifestaciones neurológicas, siendo la encefalopatía una de las más frecuentemente observadas. OBJETIVO: Se busca mostrar la utilidad del video-electroencefalograma (vEEG) en el diagnóstico de encefalopatía en pacientes ingresados por covid-19, así como su valor para determinar el pronóstico de estos pacientes. MÉTODOS: Estudio observacional retrospectivo con 76 vEEG de 41 pacientes con covid-19 confirmada. Los estudios se han realizado entre los meses de marzo del 2020 y junio del 2021. Se estudió la gravedad de la enfermedad, así como sus características clínicas y neurológicas, el tratamiento farmacológico y los hallazgos electroencefalográficos según el grado de disfunción de la encefalopatía que desarrollaron estos pacientes. RESULTADOS: De los 41 pacientes, 12 (29 %) presentaron signos electroencefalográficos de disfunción cerebral leve, 15 (37 %) disfunción cerebral moderada y 14 (34 %) disfunción cerebral severa, los cuales se asociaron con una mayor mortalidad. CONCLUSIONES: En los 76 vEEG realizados a los 41 pacientes ingresados con encefalopatías asociadas con infección por covid-19, no se observó un patrón distinto a los descritos en encefalopatías de otras etiologías. El vEEG fue útil para confirmar la sospecha clínica de una disfunción cerebral en pacientes con encefalopatías asociadas con infección por covid-19 y para asignarle un grado de severidad, confirmando su beneficio como biomarcador diagnóstico y pronóstico.

ABSTRACT INTRODUCTION: COVID-19 mainly affects the respiratory system; however, both direct and indirect involvement of the central and peripheral nervous system has also been described, causing a wide variety of neurological manifestations, with encephalopathy being one of the most frequently observed neurological manifestations. OBJECTIVE: With this article we intend to show the usefulness of vEEG in the diagnosis of encephalopathy in patients referred for COVID-19 who develop this neurological complication, as well as its value in determining the prognosis of these patients. METHODS: Retrospective observational study with 76 video-electroencephalograms of 41 patients with confirmed COVID-19 infection. The studies were performed during the months of March 2020 through June 2021. Disease severity, clinical and neurological features, pharmacological treatment and electroencephalographic indings were studied according to the degree of encephalopathy dysfunction these patients developed. RESULTS: Of the 41 patients, 12 (29 %) presented electroencephalographic signs of mild cerebral dysfunction, 15 (37 %) moderate cerebral dysfunction, and 14 (34 %) severe cerebral dysfunction, which were associated with higher mortality. CONCLUSIONS: In the 76 vEEG performed in the 41 patients admitted with encephalopathies associated with COVID-19 infection, no pattern different from that described in encephalopathies of other etiologies was observed. The vEEG was useful to confirm the clinical suspicion of brain dysfunction in patients with encephalopathies associated with COVID-19 infection and to assign a degree of severity, confirming its benefit as a diagnostic and prognostic biomarker.

The convolutional neural network as a tool to classify electroencephalography data resulting from the consumption of juice sweetened with caloric or non-caloric sweeteners.

Sweetener type can influence sensory properties and consumer's acceptance and preference for low-calorie products. An ideal sweetener does not exist, and each sweetener must be used in situations to which it is best suited. Aspartame and sucralose can be good substitutes for sucrose in passion fruit juice. Despite the interest in artificial sweeteners, little is known about how artificial sweeteners are processed in the human brain. Here, we applied the convolutional neural network (CNN) to evaluate brain signals of 11 healthy subjects when they tasted passion fruit juice equivalently sweetened with sucrose (9.4 g/100 g), sucralose (0.01593 g/100 g), or aspartame (0.05477 g/100 g). Electroencephalograms were recorded for two sites in the gustatory cortex (i.e., C3 and C4). Data with artifacts were disregarded, and the artifact-free data were used to feed a Deep Neural Network with tree branches that applied a Convolutions and pooling for different feature filtering and selection. The CNN received raw signal as input for multiclass classification and with supervised training was able to extract underling features and patterns from the signal with better performance than handcrafted filters like FFT. Our results indicated that CNN is an useful tool for electroencephalography (EEG) analyses and classification of perceptually similar tastes.

Automatic audiometry using auditory steady-state response and sequential test strategy applied to volunteers with normal hearing.

PURPOSE: In the present study, a new procedure to perform automatic audiometry using multifrequency Auditory Steady-State Response (ASSR) is proposed. METHODS: The automatic audiometry procedure consists of detecting the presence of multifrequency ASSR in real-time using the sequential test strategy and by adjusting the stimulus intensity independently. The ASSR audiometric thresholds of 18 adult volunteers with normal hearing were determined by automatically (four simultaneous frequencies per ear) at modulation frequencies in the 80 Hz range. The exam time and the difference between ASSR thresholds and pure-tone behavioural hearing thresholds were estimated as performance measures. RESULTS: The results showed that automatic audiometry can reduce the number of intensity levels used to obtain the ASSR threshold by up to 58% when compared to audiometry without using the techniques applied in automatic audiometry. In addition, the average of the difference between ASSR thresholds and Pure-Tone Behavioural Hearing thresholds was around 19 dB, which is similar to the results reported in similar studies. CONCLUSIONS: The audiometric procedure proposed in this study is fully automatic, i.e., does not require any human supervision throughout the exam, and is able to significantly reduce the conventional exam time.

A motor imagery vs. rest dataset with low-cost consumer grade EEG hardware.

The data consist of electroencephalography (EEG) signals acquired by means of low-cost consumer-grade devices from 10 participants (four females, right-handed, mean age ± SD = 26.1 ± 4.0 years) without any previous experience in Brain-Computer Interfaces (BCIs) usage. The BCI protocol consisted of two conditions, namely the kinesthetic imagination of grasping movement (motor imagery, MI) of the dominant hand and a rest/idle condition. Five protocol runs were required to be performed by each participant in a single-day session, of about 1.5 h. The first run, called RUN0, involved 5 trials of real grasping movement together with the same number of trials in a rest condition. This first run was done to both better explain the protocol and to encourage the participant to focus on the sensation of executing the movement. The rest of the runs (RUN1-RUN4) were identical, consisting of 20 trials for each condition presented in a random order. The electrical brain activity was registered from 15 electrodes covering the sensorimotor area, at a sampling frequency of 125 Hz. Muscle activity of the dominant hand was controlled via the electromyography (EMG) activity by two electrodes placed at two antagonist muscles involved in the flexion/extension of the wrist. The recordings were performed in a non-shielded office, by means of low-cost consumer grade devices and free multi-platform open source software. The EMG corruption level was analyzed and EEG trials for which the EMG activity was higher than a prescribed threshold value, were discarded. During acquisition, EEG data was digitally band-pass filtered between 0.5 and 45 Hz. These data provide a motor imagery vs. rest EEG dataset, relevant for BCI for motor rehabilitation applications. Since the recordings were performed by means of low-cost consumer grade devices in a non-controlled environment, this dataset provides an excellent source for exploring robust brain decoding techniques for future in-home BCI usage.

BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors.

Brain-computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex's electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55-63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient's perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient's performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject's MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.

Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.

In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.

Focal epilepsy features in a child with Congenital Zika Syndrome.

Zika virus (ZIKV) is a mosquito-borne, single-stranded DNA flavivirus that is teratogenic and neurotropic. Similar to the teratogenic effects of other TORCH infections, ZIKV infection during pregnancy can have an adverse impact on fetal and neonatal development. Epilepsy is detected in 48-96% of children with Congenital Zika Syndrome (CZS) and microcephaly. Early epilepsy surveillance is needed in children with prenatal ZIKV exposure; yet, most ZIKV-endemic regions do not have specialist epilepsy care. Here, we describe the demographic, clinical, imaging, and EEG characteristics of a 2-year-old child with CZS and microcephaly who presented with focal epileptiform activity, suboptimal growth, and severe neurodevelopmental delays. Administration of a brief seizure questionnaire by allied health professionals to the patient's caregiver helped to characterize the child's seizure semiology and differentiate focal from generalized seizure features. A telemedicine EEG interpretation platform provided valuable diagnostic information for the patient's local pediatrician to integrate into her treatment plan. This case illustrates that CZS can present with focal epilepsy features and that a telemedicine approach can be used to bridge the gap between epilepsy specialists and local care providers in resource limited ZIKV-endemic regions to achieve better seizure control in children with CZS.

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals.

Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

Análisis de señales electroencefalográficas para la clasificación de habla imaginada / Electroencephalographic Signals Analysis for Imagined Speech Classification

El presente trabajo tiene como objetivo interpretar las señales de EEG registradas durante la pronunciación imaginada de palabras de un vocabulario reducido, sin emitir sonidos ni articular movimientos (habla imaginada o no pronunciada) con la intención de controlar un dispositivo. Específicamente, el vocabulario permitiría controlar el cursor de la computadora, y consta de las palabras del lenguaje español: "arriba", "abajo", "izquierda", "derecha", y "seleccionar". Para ello, se registraron las señales de EEG de 27 individuos utilizando un protocolo básico para saber a priori en qué segmentos de la señal la persona imagina la pronunciación de la palabra indicada. Posteriormente, se utiliza la transformada wavelet discreta (DWT) para extraer características de los segmentos que son usados para calcular la energía relativa wavelet (RWE) en cada una de los niveles en los que la señal es descompuesta, y se selecciona un subconjunto de valores RWE provenientes de los rangos de frecuencia menores a 32 Hz. Enseguida, éstas se concatenan en dos configuraciones distintas: 14 canales (completa) y 4 canales (los más cercanos a las áreas de Broca y Wernicke). Para ambas configuraciones se entrenan tres clasificadores: Naive Bayes (NB), Random Forest (RF) y Máquina de vectores de soporte (SVM). Los mejores porcentajes de exactitud se obtuvieron con RF cuyos promedios fueron 60.11% y 47.93% usando las configuraciones de 14 canales y 4 canales, respectivamente. A pesar de que los resultados aún son preliminares, éstos están arriba del 20%, es decir, arriba del azar para cinco clases. Con lo que se puede conjeturar que las señales de EEG podrían contener información que hace posible la clasificación de las pronunciaciones imaginadas de las palabras del vocabulario reducido.

This work aims to interpret the EEG signals associated with actions to imagine the pronunciation of words that belong to a reduced vocabulary without moving the articulatory muscles and without uttering any audible sound (imagined or unspoken speech). Specifically, the vocabulary reflects movements to control the cursor on the computer, and consists of the Spanish language words: "arriba", "abajo", "izquierda", "derecha", and "seleccionar". To do this, we have recorded EEG signals from 27 subjects using a basic protocol to know a priori in what segments of the signal a subject imagines the pronunciation of the indicated word. Subsequently, discrete wavelet transform (DWT) is used to extract features from the segments. These are used to compute relative wavelet energy (RWE) in each of the levels in that EEG signal is decomposed and, it is selected a RWE values subset with the frequencies smaller than 32 Hz. Then, these are concatenated in two different configurations: 14 channels (full) and 4 channels (the channels nearest to the brain areas of Wernicke and Broca). The following three classifiers were trained using both configurations: Naive Bayes (NB), Random Forest (RF) and support vector machines (SVM). The best accuracies were obtained by RF whose averages were 60.11% and 47.93% using both configurations, respectively. Even though, the results are still preliminary, these are above 20%, this means they are more accurate than chance for five classes. Based on them, we can conjecture that the EEG signals could contain information needed for the classification of the imagined pronunciations of the words belonging to a reduced vocabulary.