ERP detection based on smoothness priors

Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. Methods: The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under the Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in post-stimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. Results: The presented method performs better than the competing algorithms and improves the classification accuracy. Conclusion: SGLRT can be employed as a powerful means for different ERP detection schemes. Significance: The proposed scheme is opening a new direction in ERP identification which provides better classification results compared to several popular ERP detection methods

An Electroencephalographic Investigation of the Encoding of Sound Source Elevation in the Human Cortex

Sound localization is of great ecological importance because it provides spa- tial perception outside the visual field. However, unlike other sensory systems, the auditory system does not represent the location of a stimulus on the level of the sensory epithelium in the cochlea. Instead, the position of a sound source has to be computed based on different localization cues. Different cues are informative of a sound sources azimuth and elevation, which, when taken together, describe the sources location in a polar coordinate system. There is a body of knowledge regarding the acoustical cues and the neural circuits in the brainstem required to perceive sound source azimuth and elevation. However, our understanding of the encoding of sound source location on the level of the cortex is lacking especially what concerns elevation. Within the scope of this thesis, we established an experimental setup to study auditory spatial perception while recording the listeners brain activity using electroencephalography. We conducted two experiments on the encoding of sound source elevation in the human cortex. Both experiments results are compatible with the hypothesis that the cortex represents sound source elevation in a population rate code where the response amplitude decreases linearly with increasing elevation. Decoding of the recorded brain activity revealed that a distinct neural representation of differently elevated sound sources was predictive of behavioral performance. An exploratory analysis indicated an increase in the amplitude of oscillations in visual areas when the subject localized sounds during eccentric eye positions. More research in this direction could help shed light on the interactions between the visual and auditory systems regarding spatial perception. The experiments presented in this dissertation are, to our knowledge, the first studies that demonstrate the encoding of sound source elevation in the human cortex by using a direct measure of neural activity (i.e., electroencephalography).:Abstract . . . . . . . . . . . . . . . . . . . . . . 1 Zusammenfassung . . . . . . . . . . . . . . . . . . . . . . 7 1 Electroencephalography 13 1.1 Event Related Potentials and Oscillations . . . . . . . . . . . . 13 1.2 Comparison to other Methods . . . . . . . . . . . . . . . . . . . 14 1.3 EEG Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4.1 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.4.2 Referencing . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.4.3 Eye Blinks . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.4.4 Epoch Rejection . . . . . . . . . . . . . . . . . . . . . . . 22 1.4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.5 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.5.1 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.5.2 Nonparametric Permutation Testing . . . . . . . . . . . 26 1.5.3 Source Separation . . . . . . . . . . . . . . . . . . . . . . 28 2 Sound Localization in the Brain . . . . . . . . . . . . . . . . . . . . 31 2.1 The Spatial Perception of Sound . . . . . . . . . . . . . . . . . . 32 2.1.1 Interaural Cues . . . . . . . . . . . . . . . . . . . . . . . 32 2.1.2 Spectral Cues . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Brain Mechanisms for Sound Localization . . . . . . . . . . . . 37 2.2.1 Auditory Pathway . . . . . . . . . . . . . . . . . . . . . 38 2.2.2 Extracting Localization Cues . . . . . . . . . . . . . . . 40 2.2.3 Neural Representation of Auditory Space . . . . . . . . 42 2.2.4 The Dual Pathway Model . . . . . . . . . . . . . . . . . 45 2.2.5 A Dominant Hemisphere for Sound Localization? . . . 47 2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3 A Free Field Setup for Psychoacoustics 51 3.1 Design of the Experimental Setup . . . . . . . . . . . . . . . . . 51 3.1.1 Loudspeakers . . . . . . . . . . . . . . . . . . . . . . . . 54 3.1.2 Processors . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.1.3 Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.1.4 Coordinate Systems . . . . . . . . . . . . . . . . . . . . 56 3.2 Operating the Setup . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.2.1 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.2.2 Loudspeaker Equalization . . . . . . . . . . . . . . . . . 59 3.3 Head Pose Estimation . . . . . . . . . . . . . . . . . . . . . . . 61 3.3.1 Landmark Detection . . . . . . . . . . . . . . . . . . . . 62 3.3.2 Perspective-n-Point Problem . . . . . . . . . . . . . . . 62 3.3.3 Camera-to-World Conversion . . . . . . . . . . . . . . . 63 3.4 A Toolbox for Psychoacoustics . . . . . . . . . . . . . . . . . . 64 4 A Linear Population Rate Code for Elevation 67 4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.1.2 Experimental Protocol . . . . . . . . . . . . . . . . . . . 69 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2.1 Behavioral Performance . . . . . . . . . . . . . . . . . . 70 4.2.2 ERP Components . . . . . . . . . . . . . . . . . . . . . . 70 4.2.3 Elevation Encoding . . . . . . . . . . . . . . . . . . . . . 72 4.2.4 Effect of Eye-Position . . . . . . . . . . . . . . . . . . . . 74 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Decoding of Brain Responses Predicts Localization Accuracy . . . 81 5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.1.2 Experimental Protocol . . . . . . . . . . . . . . . . . . . 82 5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.2.1 Behavioral Performance . . . . . . . . . . . . . . . . . . 83 5.2.2 ERP Components . . . . . . . . . . . . . . . . . . . . . . 84 5.2.3 Decoding Brain Activity . . . . . . . . . . . . . . . . . . 86 5.2.4 Topography of Elevation Encoding . . . . . . . . . . . . 88 5.2.5 Elevation Tuning . . . . . . . . . . . . . . . . . . . . . . 89 5.2.6 Hemispheric Lateralization . . . . . . . . . . . . . . . . 91 5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 A Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 B Publication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Novas estratégias de pré-processamento, extração de atributos e classificação em sistemas BCI

Orientador: Romis Ribeiro de Faissol AttuxTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: As interfaces cérebro-computador (BCIs) visam controlar um dispositivo externo, utilizando diretamente os sinais cerebrais do usuário. Tais sistemas requerem uma série de etapas para processar e extrair atributos relevantes dos sinais observados para interpretar correta e eficientemente as intenções do usuário. Embora o campo tenha se desenvolvido continuamente e algumas dificuldades tenham sido superadas, ainda é necessário aumentar a capacidade de uso, melhorando sua capacidade de classificação e aumentando a confiabilidade de sua resposta. O objetivo clássico da pesquisa de BCI é apoiar a comunicação e o controle para usuários com comunicação prejudicada devido a doenças ou lesões. Aplicações típicas das BCI são a operação de cursores de interface, programas de escrita de texto ou dispositivos externos, como cadeiras de rodas, robôs e diferentes tipos de próteses. O usuário envia informações moduladas para a BCI, realizando tarefas mentais que produzem padrões cerebrais distintos. A BCI adquire sinais do cérebro do usuário e os traduz em comunicação adequada. Esta tese tem como objetivo desenvolver uma comunicação BCI não invasiva mais rápida e confiável baseada no estudo de diferentes técnicas que atuam nas etapas de processamento do sinal, considerando dois aspectos principais, a abordagem de aprendizado de máquina e a redução da complexidade na tarefa de aprendizado dos padrões mentais pelo usuário. A pesquisa foi focada em dois paradigmas de BCI, Imagética Motora (IM) e o potencial relacionado ao evento P300. Algoritmos de processamento de sinais para a detecção de ambos os padrões cerebrais foram aplicados e avaliados. O aspecto do pré-processamento foi a primeira perspectiva estudada, considerando como destacar a resposta dos fenômenos cerebrais, em relação ao ruído e a outras fontes de informação que talvez distorçam o sinal de EEG; isso em si é um passo que influenciará diretamente a resposta dos seguintes blocos de processamento e classificação. A Análise de Componente Independente (ICA) foi usada em conjunto com métodos de seleção de atributos e diferentes classificadores para separar as fontes originais relacionadas à dessincronização produzida pelo fenômeno de IM; esta foi uma tentativa de criar um tipo de filtro espacial que permitisse o sinal ser pré-processado, reduzindo a influência do ruído. Além disso, os resultados dos valores de classificação foram analisados considerando a comparação com métodos padrão de pré-processamento, como o filtro CAR. Os resultados mostraram que é possível separar os componentes relacionados à atividade motora. A proposta da ICA, em média, foi 4\% mais alta em porcentagem de precisão de classificação do que os resultados obtidos usando o CAR, ou quando nenhum filtro foi usado. O papel dos métodos que estudam a conectividade de diferentes áreas do cérebro foi avaliado como a segunda contribuição deste trabalho; Isso permitiu considerar aspectos que contemplam a complexidade da resposta cerebral de um usuário. A área da BCI precisa de uma interpretação mais profunda do que acontece no nível do cérebro em vários dos fenômenos estudados. A técnica utilizada para construir grafos de conectividade funcional foi a correntropia, esta medida foi utilizada para quantificar a similaridade; uma comparação foi feita usando também, as medidas de correlação de Spearman e Pearson. A conectividade funcional relaciona diferentes áreas do cérebro analisando sua atividade cerebral, de modo que o estudo do grafo foi avaliado utilizando três medidas de centralidade, onde a importância de um nó na rede é medida. Também, dois tipos de classificadores foram testados, comparando os resultados no nível de precisão de classificação. Em conclusão, a correntropia pode trazer mais informações para o estudo da conectividade do que o uso da correlação simples, o que trouxe melhorias nos resultados da classificação, especialmente quando ela foi utilizada com o classificador ELM. Finalmente, esta tese demonstra que os BCIs podem fornecer comunicação efetiva em uma aplicação onde a predição da resposta de classificação foi modelada, o que permitiu a otimização dos parâmetros do processamento de sinal realizado usando o filtro espacial xDAWN e um classificador FLDA para o problema do speller P300, buscando a melhor resposta para cada usuário. O modelo de predição utilizado foi Bayesiano e confirmou os resultados obtidos com a operação on-line do sistema, permitindo otimizar os parâmetros tanto do filtro quanto do classificador. Desta forma, foi visto que usando filtros com poucos canais de entrada, o modelo otimizado deu melhores resultados de acurácia de classificação do que os valores inicialmente obtidos ao treinar o filtro xDAWN para os mesmos casos. Os resultados obtidos mostraram que melhorias nos métodos do transdutor BCI, no pré-processamento, extração de características e classificação constituíram a base para alcançar uma comunicação BCI mais rápida e confiável. O avanço nos resultados da classificação foi obtido em todos os casos, comparado às técnicas que têm sido amplamente utilizadas e já mostraram eficácia para esse tipo de problema. No entanto, ainda há aspectos a considerar da resposta dos sujeitos para tipos específicos de paradigmas, lembrando que sua resposta pode variar ao longo de diferentes dias e as implicações reais disso na definição e no uso de diferentes métodos de processamento de sinalAbstract: Brain-computer interfaces (BCIs) aim to control an external device by directly employing user's brain signals. Such systems require a series of steps to process and extract relevant features from the observed signals to correctly and efficiently interpret the user's intentions. Although the field has been continuously developing and some difficulties have been overcome, it is still necessary to increase usability by enhancing their classification capacity and increasing the reliability of their response. The classical objective of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of interface cursors, spelling programs or external devices, such as wheelchairs, robots and different types of prostheses. The user sends modulated information to the BCI by engaging in mental tasks that produce distinct brain patterns. The BCI acquires signals from the user¿s brain and translates them into suitable communication. This thesis aims to develop faster and more reliable non-invasive BCI communication based on the study of different techniques that serve in the signal processing stages, considering two principal aspects, the machine learning approach, and the reduction of the complexity in the task of learning the mental patterns by the user. Research was focused on two BCI paradigms, Motor Imagery (MI) and the P300 event related potential (ERP). Signal processing algorithms for the detection of both brain patterns were applied and evaluated. The aspect of the pre-processing was the first perspective studied to consider how to highlight the response of brain phenomena, in relation to noise and other sources of information that maybe distorting the EEG signal; this in itself is a step that will directly influence the response of the following blocks of processing and classification. The Independent Component Analysis (ICA) was used in conjunction with feature selection methods and different classifiers to separate the original sources that are related to the desynchronization produced by MI phenomenon; an attempt was made to create a type of spatial filter that pre-processed the signal, reducing the influence of the noise. Furthermore, some of the classifications values were analyzed considering comparison when used other standard pre-processing methods, as the CAR filter. The results showed that it is possible to separate the components related to motor activity. The ICA proposal on average were 4\% higher in percent of classification accuracy than those obtained using CAR, or when no filter was used. The role of methods that study the connectivity of different brain areas were evaluated as the second contribution of this work; this allowed to consider aspects that contemplate the complexity of the brain response of a user. The area of BCI needs a deeper interpretation of what happens at the brain level in several of the studied phenomena. The technique used to build functional connectivity graphs was correntropy, this quantity was used to measure similarity, a comparison was made using also, the Spearman and Pearson correlation. Functional connectivity relates different brain areas activity, so the study of the graph was evaluated using three measures of centrality of graph, where the importance of a node in the network is measured. In addition, two types of classifiers were tested, comparing the results at the level of classification precision. In conclusion, the correntropy can bring more information for the study of connectivity than the use of the simple correlation, which brought improvements in the classification results especially when it was used with the ELM classifier. Finally, this thesis demonstrates that BCIs can provide effective communication in an application where the prediction of the classification response was modeled, which allowed the optimization of the parameters of the signal processing performed using the xDAWN spatial filter and a FLDA classifier for the problem of the P300 speller, seeking the best response for each user. The prediction model used was Bayesian and confirmed the results obtained with the on-line operation of the system, thus allowing to optimize the parameters of both the filter and the classifier. In this way it was seen that using filters with few inputs the optimized model gave better results of acuraccy classification than the values initially obtained when the training ofthe xDAWN filter was made for the same cases. The obtained results showed that improvements in the BCI transducer, pre-processing, feature extraction and classification methods constituted the basis to achieve faster and more reliable BCI communication. The advance in the classification results were obtained in all cases, compared to techniques that have been widely used and had already shown effectiveness for this type of problemsDoutoradoEngenharia de ComputaçãoDoutora em Engenharia Elétrica153311/2014-2CNP

Development of a passive MEG stimulus for measurement of the binaural masking level difference

The ability to hear a target signal over background noise is an important aspect of efficient hearing in everyday situations. This mechanism depends on binaural hearing whenever there are differences in the inter-aural timing of inputs from the noise and the signal. Impairments in binaural hearing may underlie some auditory processing disorders, for example temporal-lobe epilepsies. The binaural masking level difference (BMLD) measures the advantage in detecting a tone whose inter-aural phase differs from that of the masking noise. BMLD’s are typically estimated psychophysically, but this is challenging in children or those with cognitive impairments. The aim of this doctorate is to design a passive measure of BMLD using magnetoencephalography (MEG) and test this in adults, children and patients with different types of epilepsy. The stimulus consists of Gaussian background noise with 500-Hz tones presented binaurally either in-phase or 180° out-of-phase between the ears. Source modelling provides the N1m amplitude for the in-phase and out-of-phase tones, representing the extent of signal perception over background noise. The passive BMLD stimulus is successfully used as a measure of binaural hearing capabilities in participants who would otherwise be unable to undertake a psychophysical task

Slow-wave activity in the S1HL cortex is contributed by different layer-specific field potential sources during development

Spontaneous correlated activity in cortical columns is criticalfor postnatal circuit refinement.We used spatial discriminationtechniques to explore the late maturation of synaptic pathways through the laminar distribution of the field potential (FP) generators underlying spontaneous and evoked activities ofthe S1HL cortex in juvenile (P14 –P16) and adult anesthetized rats. Juveniles exhibit an intermittent FP pattern resembling Up/Down states in adults, but with much reduced power and different laminar distribution. Whereas FPs in active periods are dominated by a layer VI generator in juveniles, in adults a developing multipart generatortakes over, displaying current sinks in middle layers (III–V). The blockade of excitatory transmission in upper and middle layers of adults recovered the juvenile-like FP profiles. In additiontothe layer VI generator, a gamma-specific generator in supragranular layers wasthe same in both age groups.While searching for dynamical coupling among generators in juveniles we found significant cross-correlation in one-half of the tested pairs, whereas excessive coherence hindered their efficient separation in adults. Also, potentials evoked by tactile and electrical stimuli showed different short-latency dipoles between the two age groups, and the juveniles lacked the characteristic long latency UP state currents in middle layers. In addition, the mean firing rate of neurons was lower in juveniles. Thus, cortical FPs originate from different intracolumnar segments as they become active postnatally. We suggest that although some cortical segments are active early postnatally, a functional sensory-motor control relies on a delayed maturation and network integration of synaptic connections in middle layers

Aerospace medicine and biology: A cumulative index to the continuing bibliography of the 1973 issues

A cumulative index to the abstracts contained in Supplements 112 through 123 of Aerospace Medicine and Biology A Continuing Bibliography is presented. It includes three indexes: subject, personal author, and corporate source

OPTIMIZATION OF TIME-RESPONSE AND AMPLIFICATION FEATURES OF EGOTs FOR NEUROPHYSIOLOGICAL APPLICATIONS

In device engineering, basic neuron-to-neuron communication has recently inspired the development of increasingly structured and efficient brain-mimicking setups in which the information flow can be processed with strategies resembling physiological ones. This is possible thanks to the use of organic neuromorphic devices, which can share the same electrolytic medium and adjust reciprocal connection weights according to temporal features of the input signals. In a parallel - although conceptually deeply interconnected - fashion, device engineers are directing their efforts towards novel tools to interface the brain and to decipher its signalling strategies. This led to several technological advances which allow scientists to transduce brain activity and, piece by piece, to create a detailed map of its functions. This effort extends over a wide spectrum of length-scales, zooming out from neuron-to-neuron communication up to global activity of neural populations. Both these scientific endeavours, namely mimicking neural communication and transducing brain activity, can benefit from the technology of Electrolyte-Gated Organic Transistors (EGOTs). Electrolyte-Gated Organic Transistors (EGOTs) are low-power electronic devices that functionally integrate the electrolytic environment through the exploitation of organic mixed ionic-electronic conductors. This enables the conversion of ionic signals into electronic ones, making such architectures ideal building blocks for neuroelectronics. This has driven extensive scientific and technological investigation on EGOTs. Such devices have been successfully demonstrated both as transducers and amplifiers of electrophysiological activity and as neuromorphic units. These promising results arise from the fact that EGOTs are active devices, which widely extend their applicability window over the capabilities of passive electronics (i.e. electrodes) but pose major integration hurdles. Being transistors, EGOTs need two driving voltages to be operated. If, on the one hand, the presence of two voltages becomes an advantage for the modulation of the device response (e.g. for devising EGOT-based neuromorphic circuitry), on the other hand it can become detrimental in brain interfaces, since it may result in a non-null bias directly applied on the brain. If such voltage exceeds the electrochemical stability window of water, undesired faradic reactions may lead to critical tissue and/or device damage. This work addresses EGOTs applications in neuroelectronics from the above-described dual perspective, spanning from neuromorphic device engineering to in vivo brain-device interfaces implementation. The advantages of using three-terminal architectures for neuromorphic devices, achieving reversible fine-tuning of their response plasticity, are highlighted. Jointly, the possibility of obtaining a multilevel memory unit by acting on the gate potential is discussed. Additionally, a novel mode of operation for EGOTs is introduced, enabling full retention of amplification capability while, at the same time, avoiding the application of a bias in the brain. Starting on these premises, a novel set of ultra-conformable active micro-epicortical arrays is presented, which fully integrate in situ fabricated EGOT recording sites onto medical-grade polyimide substrates. Finally, a whole organic circuitry for signal processing is presented, exploiting ad-hoc designed organic passive components coupled with EGOT devices. This unprecedented approach provides the possibility to sort complex signals into their constitutive frequency components in real time, thereby delineating innovative strategies to devise organic-based functional building-blocks for brain-machine interfaces.Nell’ingegneria elettronica, la comunicazione di base tra neuroni ha recentemente ispirato lo sviluppo di configurazioni sempre più articolate ed efficienti che imitano il cervello, in cui il flusso di informazioni può essere elaborato con strategie simili a quelle fisiologiche. Ciò è reso possibile grazie all'uso di dispositivi neuromorfici organici, che possono condividere lo stesso mezzo elettrolitico e regolare i pesi delle connessioni reciproche in base alle caratteristiche temporali dei segnali in ingresso. In modo parallelo, gli ingegneri elettronici stanno dirigendo i loro sforzi verso nuovi strumenti per interfacciare il cervello e decifrare le sue strategie di comunicazione. Si è giunti così a diversi progressi tecnologici che consentono agli scienziati di trasdurre l'attività cerebrale e, pezzo per pezzo, di creare una mappa dettagliata delle sue funzioni. Entrambi questi ambiti scientifici, ovvero imitare la comunicazione neurale e trasdurre l'attività cerebrale, possono trarre vantaggio dalla tecnologia dei transistor organici a base elettrolitica (EGOT). I transistor organici a base elettrolitica (EGOT) sono dispositivi elettronici a bassa potenza che integrano funzionalmente l'ambiente elettrolitico attraverso lo sfruttamento di conduttori organici misti ionici-elettronici, i quali consentono di convertire i segnali ionici in segnali elettronici, rendendo tali dispositivi ideali per la neuroelettronica. Gli EGOT sono stati dimostrati con successo sia come trasduttori e amplificatori dell'attività elettrofisiologica e sia come unità neuromorfiche. Tali risultati derivano dal fatto che gli EGOT sono dispositivi attivi, al contrario dell'elettronica passiva (ad esempio gli elettrodi), ma pongono comunque qualche ostacolo alla loro integrazione in ambiente biologico. In quanto transistor, gli EGOT necessitano l'applicazione di due tensioni tra i suoi terminali. Se, da un lato, la presenza di due tensioni diventa un vantaggio per la modulazione della risposta del dispositivo (ad esempio, per l'ideazione di circuiti neuromorfici basati su EGOT), dall'altro può diventare dannosa quando gli EGOT vengono adoperati come sito di registrazione nelle interfacce cerebrali, poiché una tensione non nulla può essere applicata direttamente al cervello. Se tale tensione supera la finestra di stabilità elettrochimica dell'acqua, reazioni faradiche indesiderate possono manifestarsi, le quali potrebbero danneggiare i tessuti e/o il dispositivo. Questo lavoro affronta le applicazioni degli EGOT nella neuroelettronica dalla duplice prospettiva sopra descritta: ingegnerizzazione neuromorfica ed implementazione come interfacce neurali in applicazioni in vivo. Vengono evidenziati i vantaggi dell'utilizzo di architetture a tre terminali per i dispositivi neuromorfici, ottenendo una regolazione reversibile della loro plasticità di risposta. Si discute inoltre la possibilità di ottenere un'unità di memoria multilivello agendo sul potenziale di gate. Viene introdotta una nuova modalità di funzionamento per gli EGOT, che consente di mantenere la capacità di amplificazione e, allo stesso tempo, di evitare l'applicazione di una tensione all’interfaccia cervello-dispositivo. Partendo da queste premesse, viene presentata una nuova serie di array micro-epicorticali ultra-conformabili, che integrano completamente i siti di registrazione EGOT fabbricati in situ su substrati di poliimmide. Infine, viene proposto un circuito organico per l'elaborazione del segnale, sfruttando componenti passivi organici progettati ad hoc e accoppiati a dispositivi EGOT. Questo approccio senza precedenti offre la possibilità di filtrare e scomporre segnali complessi nelle loro componenti di frequenza costitutive in tempo reale, delineando così strategie innovative per concepire blocchi funzionali a base organica per le interfacce cervello-macchina