Human scalp potentials reflect a mixture of decision-related signals during perceptual choices

Single-unit animal studies have consistently reported decision-related activity mirroring a process of temporal accumulation of sensory evidence to a fixed internal decision boundary. To date, our understanding of how response patterns seen in single-unit data manifest themselves at the macroscopic level of brain activity obtained from human neuroimaging data remains limited. Here, we use single-trial analysis of human electroencephalography data to show that population responses on the scalp can capture choice-predictive activity that builds up gradually over time with a rate proportional to the amount of sensory evidence, consistent with the properties of a drift-diffusion-like process as characterized by computational modeling. Interestingly, at time of choice, scalp potentials continue to appear parametrically modulated by the amount of sensory evidence rather than converging to a fixed decision boundary as predicted by our model. We show that trial-to-trial fluctuations in these response-locked signals exert independent leverage on behavior compared with the rate of evidence accumulation earlier in the trial. These results suggest that in addition to accumulator signals, population responses on the scalp reflect the influence of other decision-related signals that continue to covary with the amount of evidence at time of choice

Reliability of Graph Measures Derived from Resting-State MEG Data Using Source Space Functional Connectivity Analysis

The reliability of global graph measures derived from neuroimaging data is an important criterion for their use as biomarkers for neurological disorders. This study examined the reliability of the global efficiency (GE), characteristic path length (CPL), transitivity, and synchronizability of functional whole-brain and intra-hemispheric networks based on resting-state magnetoencephalography. Brain sources were reconstructed using atlas-based beamforming, and functional connectivity in six frequency bands was estimated using the debiased weighted phase lag index. An optimal threshold of 100% was chosen based on test-retest reliability of the measures. At this threshold, test-retest reliability of the GE, CPL, and transitivity was mostly fair to excellent except for in the delta band. However, test-retest reliability of the synchronizability was mostly poor to fair. There was no significant effect of gender on any graph measure. Overall, these results indicate that the GE, CPL, and transitivity in most of the frequency bands may be useful biomarkers

Best practices for fNIRS publications

The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers

Tracking brain dynamics across transitions of consciousness

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College

Data Augmentation for Deep-Learning-Based Electroencephalography

Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis

Data Augmentation for Deep-Learning-Based Electroencephalography

Background: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)—increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. New method: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? Results: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. Comparing with existing methods: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload—29% on average. Conclusions: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications—if adhering to our reporting guidelines—will facilitate more detailed analysis

A Method for Measuring Puffing and Respiratory Parameters of Tobacco Users within their Natural Usage Environment

Background: Although researchers have investigated the puffing behavior of tobacco products, no attempt has yet been made to observe both puffing and respiratory behaviors simultaneously in the natural use environment. Observation of puffing behavior alone is insufficient for predicting the health effects of tobacco use, as it can only be used to estimate the amount of emissions generated and transferred to the oral cavity. Respiration behavior must be observed for the estimation of delivery and retention of nicotine and other HPHCs in the lungs. Parameters that quantify respiratory behavior include inhalation and exhalation volumes, flow rates, and durations, as well as breath-hold duration. Researchers are presently limited by the lack of a viable non-invasive ambulatory monitoring technique for simultaneous monitoring of puffing and respiratory behavior. Methodologies: The primary focus of this work is in adapting a commercially-available Wearable Respiratory Monitor (WRM) to measure quantitative respiratory parameters. These devices normally only report basic metrics such as respiratory-rate. They are, however, equipped with sensors that track chest motion which can be used to infer respiratory volume via calibration. Nine commercial WRMs were identified. By employing a selection criteria, three WRM candidates were acquired for extensive characterization using a purpose-built chest expansion simulator. To measure puffing parameters, the previously validated and deployed wPUM topography monitor was used. Parameters based on puffing and respiratory behaviors were proposed for quantifying the specific puffing and inhalation patterns of Mouth-to-Lung (MTL) and Direct-to-Lung (DTL). A method was developed to synchronize the data collected from the Hexoskin to that of the wPUM to account for discrepancies in their real-time clocks. Data processing tools were developed to perform the various analyses and signal processing tasks. Results: The Hexoskin Smart Garment was determined to be the most suitable WRM. The device was successfully calibrated and although the calibration parameters showed some variability across repeated trials, the overall impact of this on the measurement of respiratory volume was determined to be relatively low. The Hexoskin was validated against a spirometer and was found to have good accuracy and repeatability. Respiratory parameters were calculated from data collected over a period of 12 hours (over 10,000 breaths) in the natural environment. The time synchronization method proved to be effective at eliminating the time discrepancy between the Hexoskin and the wPUM monitor. The combined system was able to find puff associated respiratory cycles from participant data. Application: This combined system has been deployed in two studies to help assess the influence of tobacco product characteristics, specifically flow path resistance and nicotine strength on puffing and respiratory behavior. Previous research suggest that users of products with high flow path resistance, such as cigarettes, are more likely to exhibit MTL behavior whereas users of products with low flow path resistance, such as hookah, are more likely to exhibit DTL behavior. A reduction in nicotine strength may cause users to perform compensatory behaviors, such as taking larger inhale volumes and holding their breaths for longer. The system, with some improvements, would be useful to the tobacco research community

Investigating trait impulsivity:behavioural and neural differences in a non-clinical population

The aim of this thesis was to conduct a comprehensive investigation of impulsivity, including rapid-response and reward-delay impulsivity dimensions. A behavioural study was conducted to examine the sensitivity of impulsivity measures to differences between low and high impulsivity groups, based on impulsivity questionnaires. Results showed that the proposed measures were sensitive to differences between groups and that combining impulsivity dimensions provided a better predictor of impulsivity level than each dimension alone. We then tested whether a three-factor model of impulsivity, would benefit or not from the inclusion of a psychometric measure of reward-delay. Although results favoured a three-factor model, including the reward-delay psychometric measure did not improve the model fit, and showed that rapid-response and reward-delay impulsivity are two major dimensions which contribute independently to impulsivity. Potential differences in the neural correlates of response inhibition and delay discounting between the two groups were examined using MEG. Results suggested high impulsivity individuals might show an attentional processing deficit, as indicated by smaller M1 components and less alpha suppression in posterior regions in the two tasks. Regarding response inhibition, the M2 component was found to be reduced in individuals scoring high, possibly reflecting less efficiency. The high impulsivity group engaged frontal networks more during the STOP-M3 component only, possibly as a compensatory strategy. Increased preference for immediacy was observed in high impulsivity individuals, as reflected by larger Immediate-M2 amplitudes. Decreased delta and theta band power was observed in high impulsivity individuals, suggesting a possible deficit in frontal pathways involved in motor suppression. Increased delta and theta power were observed in frontal regions in high impulsivity individuals, while beta band power was found to be supressed, suggesting an increased sensitivity towards reward-related cues. The experiments described here illustrated how trait impulsivity relates to differences in the behavioural and neural correlates of cognitive processes

Brain Music : Sistema generativo para la creación de música simbólica a partir de respuestas neuronales afectivas

gráficas, tablasEsta tesis de maestría presenta una metodología de aprendizaje profundo multimodal innovadora que fusiona un modelo de clasificación de emociones con un generador musical, con el propósito de crear música a partir de señales de electroencefalografía, profundizando así en la interconexión entre emociones y música. Los resultados alcanzan tres objetivos específicos: Primero, ya que el rendimiento de los sistemas interfaz cerebro-computadora varía considerablemente entre diferentes sujetos, se introduce un enfoque basado en la transferencia de conocimiento entre sujetos para mejorar el rendimiento de individuos con dificultades en sistemas de interfaz cerebro-computadora basados en el paradigma de imaginación motora. Este enfoque combina datos de EEG etiquetados con datos estructurados, como cuestionarios psicológicos, mediante un método de "Kernel Matching CKA". Utilizamos una red neuronal profunda (Deep&Wide) para la clasificación de la imaginación motora. Los resultados destacan su potencial para mejorar las habilidades motoras en interfaces cerebro-computadora. Segundo, proponemos una técnica innovadora llamada "Labeled Correlation Alignment"(LCA) para sonificar respuestas neurales a estímulos representados en datos no estructurados, como música afectiva. Esto genera características musicales basadas en la actividad cerebral inducida por las emociones. LCA aborda la variabilidad entre sujetos y dentro de sujetos mediante el análisis de correlación, lo que permite la creación de envolventes acústicos y la distinción entre diferente información sonora. Esto convierte a LCA en una herramienta prometedora para interpretar la actividad neuronal y su reacción a estímulos auditivos. Finalmente, en otro capítulo, desarrollamos una metodología de aprendizaje profundo de extremo a extremo para generar contenido musical MIDI (datos simbólicos) a partir de señales de actividad cerebral inducidas por música con etiquetas afectivas. Esta metodología abarca el preprocesamiento de datos, el entrenamiento de modelos de extracción de características y un proceso de emparejamiento de características mediante Deep Centered Kernel Alignment, lo que permite la generación de música a partir de señales EEG. En conjunto, estos logros representan avances significativos en la comprensión de la relación entre emociones y música, así como en la aplicación de la inteligencia artificial en la generación musical a partir de señales cerebrales. Ofrecen nuevas perspectivas y herramientas para la creación musical y la investigación en neurociencia emocional. Para llevar a cabo nuestros experimentos, utilizamos bases de datos públicas como GigaScience, Affective Music Listening y Deap Dataset (Texto tomado de la fuente)This master’s thesis presents an innovative multimodal deep learning methodology that combines an emotion classification model with a music generator, aimed at creating music from electroencephalography (EEG) signals, thus delving into the interplay between emotions and music. The results achieve three specific objectives: First, since the performance of brain-computer interface systems varies significantly among different subjects, an approach based on knowledge transfer among subjects is introduced to enhance the performance of individuals facing challenges in motor imagery-based brain-computer interface systems. This approach combines labeled EEG data with structured information, such as psychological questionnaires, through a "Kernel Matching CKA"method. We employ a deep neural network (Deep&Wide) for motor imagery classification. The results underscore its potential to enhance motor skills in brain-computer interfaces. Second, we propose an innovative technique called "Labeled Correlation Alignment"(LCA) to sonify neural responses to stimuli represented in unstructured data, such as affective music. This generates musical features based on emotion-induced brain activity. LCA addresses variability among subjects and within subjects through correlation analysis, enabling the creation of acoustic envelopes and the distinction of different sound information. This makes LCA a promising tool for interpreting neural activity and its response to auditory stimuli. Finally, in another chapter, we develop an end-to-end deep learning methodology for generating MIDI music content (symbolic data) from EEG signals induced by affectively labeled music. This methodology encompasses data preprocessing, feature extraction model training, and a feature matching process using Deep Centered Kernel Alignment, enabling music generation from EEG signals. Together, these achievements represent significant advances in understanding the relationship between emotions and music, as well as in the application of artificial intelligence in musical generation from brain signals. They offer new perspectives and tools for musical creation and research in emotional neuroscience. To conduct our experiments, we utilized public databases such as GigaScience, Affective Music Listening and Deap DatasetMaestríaMagíster en Ingeniería - Automatización IndustrialInvestigación en Aprendizaje Profundo y señales BiológicasEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale