Reward prediction error and declarative memory

Learning based on reward prediction error (RPE) was originally proposed in the context of nondeclarative memory. We postulate that RPE may support declarative memory as well. Indeed, recent years have witnessed a number of independent empirical studies reporting effects of RPE on declarative memory. We provide a brief overview of these studies, identify emerging patterns, and discuss open issues such as the role of signed versus unsigned RPEs in declarative learning

Quantum calcium-ion interactions with EEG

Previous papers have developed a statistical mechanics of neocortical interactions (SMNI) fit to short-term memory and EEG data. Adaptive Simulated Annealing (ASA) has been developed to perform fits to such nonlinear stochastic systems. An N-dimensional path-integral algorithm for quantum systems, qPATHINT, has been developed from classical PATHINT. Both fold short-time propagators (distributions or wave functions) over long times. Previous papers applied qPATHINT to two systems, in neocortical interactions and financial options. \textbf{Objective}: In this paper the quantum path-integral for Calcium ions is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Using fits of this SMNI model to EEG data, including these effects, will help determine if this is a reasonable approach. \textbf{Method}: Methods of mathematical-physics for optimization and for path integrals in classical and quantum spaces are used for this project. Studies using supercomputer resources tested various dimensions for their scaling limits. In this paper the quantum path-integral is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. \textbf{Results}: The mathematical-physics and computer parts of the study are successful, in that there is modest improvement of cost/objective functions used to fit EEG data using these models. \textbf{Conclusion}: This project points to directions for more detailed calculations using more EEG data and qPATHINT at each time slice to propagate quantum calcium waves, synchronized with PATHINT propagation of classical SMNI.Comment: published in Sc

Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study.

Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine

Electrophysiology of statistical learning: Exploring the online learning process and offline learning product

First published: 24 December 2019A continuous stream of syllables is segmented into discrete constituents based on the transitional probabilities (TPs) between adjacent syllables by means of statistical learning. However, we still do not know whether people attend to high TPs between frequently co-occurring syllables and cluster them together as parts of the discrete constituents or attend to low TPs aligned with the edges between the constituents and extract them as whole units. Earlier studies on TP-based segmentation also have not distinguished between the segmentation process (how people segment continuous speech) and the learning product (what is learnt by means of statistical learning mechanisms). In the current study, we explored the learning outcome separately from the learning process, focusing on three possible learning products: holistic constituents that are retrieved from memory during the recognition test, clusters of frequently co-occurring syllables, or a set of statistical regularities which can be used to reconstruct legitimate candidates for discrete constituents during the recognition test. Our data suggest that people employ boundary-finding mechanisms during online segmentation by attending to low inter-syllabic TPs during familiarization and also identify potential candidates for discrete constituents based on their statistical congruency with rules extracted during the learning process. Memory representations of recurrent constituents embedded in the continuous speech stream during familiarization facilitate subsequent recognition of these discrete constituents.Secretaría de Estado de Investigación, Desarrollo e Innovación, Grant/Award Number: RTI2018-098317-B-I00 ; H2020 Marie Skłodowska-Curie Actions, Grant/Award Number: DLV-792331; Ekonomiaren Garapen eta Lehiakortasun Saila, Eusko Jaurlaritza, Grant/Award Number: PI-2017-25; Spanish Ministry of Economy and Competitiveness (MINECO) through the “Severo Ochoa” Programme for Centres/Units of Excellence in R&D, Grant/Award Number: SEV-2015-490; Basque Government, Grant/Award Number: PI-2017-25; European Commission through the Marie Skłodowska-Curie Research Fellowshi

BCI applications based on artificial intelligence oriented to deep learning techniques

A Brain-Computer Interface, BCI, can decode the brain signals corresponding to the intentions of individuals who have lost neuromuscular connection, to reestablish communication to control external devices. To this aim, BCI acquires brain signals as Electroencephalography (EEG) or Electrocorticography (ECoG), uses signal processing techniques and extracts features to train classifiers for providing proper control instructions. BCI development has increased in the last decades, improving its performance through the use of different signal processing techniques for feature extraction and artificial intelligence approaches for classification, such as deep learning-oriented classifiers. All of these can assure more accurate assistive systems but also can enable an analysis of the learning process of signal characteristics for the classification task. Initially, this work proposes the use of a priori knowledge and a correlation measure to select the most discriminative ECoG signal electrodes. Then, signals are processed using spatial filtering and three different types of temporal filtering, followed by a classifier made of stacked autoencoders and a softmax layer to discriminate between ECoG signals from two types of visual stimuli. Results show that the average accuracy obtained is 97% (+/- 0.02%), which is similar to state-of-the-art techniques, nevertheless, this method uses minimal prior physiological and an automated statistical technique to select some electrodes to train the classifier. Also, this work presents classifier analysis, figuring out which are the most relevant signal features useful for visual stimuli classification. The features and physiological information such as the brain areas involved are compared. Finally, this research uses Convolutional Neural Networks (CNN) or Convnets to classify 5 categories of motor tasks EEG signals. Movement-related cortical potentials (MRCPs) are used as a priori information to improve the processing of time-frequency representation of EEG signals. Results show an increase of more than 25% in average accuracy compared to a state-of-the-art method that uses the same database. In addition, an analysis of CNN or ConvNets filters and feature maps is done to and the most relevant signal characteristics that can help classify the five types of motor tasks.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Deep learning approach for epileptic seizure detection

Abstract. Epilepsy is the most common brain disorder that affects approximately fifty million people worldwide, according to the World Health Organization. The diagnosis of epilepsy relies on manual inspection of EEG, which is error-prone and time-consuming. Automated epileptic seizure detection of EEG signal can reduce the diagnosis time and facilitate targeting of treatment for patients. Current detection approaches mainly rely on the features that are designed manually by domain experts. The features are inflexible for the detection of a variety of complex patterns in a large amount of EEG data. Moreover, the EEG is non-stationary signal and seizure patterns vary across patients and recording sessions. EEG data always contain numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges deep learning approaches are examined in this paper. Deep learning methods were applied to a large publicly available dataset, the Children’s Hospital of Boston-Massachusetts Institute of Technology dataset (CHB-MIT). The present study includes three experimental groups that are grouped based on the pre-processing steps. The experimental groups contain 3–4 experiments that differ between their objectives. The time-series EEG data is first pre-processed by certain filters and normalization techniques, and then the pre-processed signal was segmented into a sequence of non-overlapping epochs. Second, time series data were transformed into different representations of input signals. In this study time-series EEG signal, magnitude spectrograms, 1D-FFT, 2D-FFT, 2D-FFT magnitude spectrum and 2D-FFT phase spectrum were investigated and compared with each other. Third, time-domain or frequency-domain signals were used separately as a representation of input data of VGG or DenseNet 1D. The best result was achieved with magnitude spectrograms used as representation of input data in VGG model: accuracy of 0.98, sensitivity of 0.71 and specificity of 0.998 with subject dependent data. VGG along with magnitude spectrograms produced promising results for building personalized epileptic seizure detector. There was not enough data for VGG and DenseNet 1D to build subject-dependent classifier.Epileptisten kohtausten havaitseminen syväoppimisella lähestymistavalla. Tiivistelmä. Epilepsia on yleisin aivosairaus, joka Maailman terveysjärjestön mukaan vaikuttaa noin viiteenkymmeneen miljoonaan ihmiseen maailmanlaajuisesti. Epilepsian diagnosointi perustuu EEG:n manuaaliseen tarkastamiseen, mikä on virhealtista ja aikaa vievää. Automaattinen epileptisten kohtausten havaitseminen EEG-signaalista voi potentiaalisesti vähentää diagnoosiaikaa ja helpottaa potilaan hoidon kohdentamista. Nykyiset tunnistusmenetelmät tukeutuvat pääasiassa piirteisiin, jotka asiantuntijat ovat määritelleet manuaalisesti, mutta ne ovat joustamattomia monimutkaisten ilmiöiden havaitsemiseksi suuresta määrästä EEG-dataa. Lisäksi, EEG on epästationäärinen signaali ja kohtauspiirteet vaihtelevat potilaiden ja tallennusten välillä ja EEG-data sisältää aina useita kohinatyyppejä, jotka huonontavat epilepsiakohtauksen havaitsemisen tarkkuutta. Näihin haasteisiin vastaamiseksi tässä diplomityössä tarkastellaan soveltuvatko syväoppivat menetelmät epilepsian havaitsemiseen EEG-tallenteista. Aineistona käytettiin suurta julkisesti saatavilla olevaa Bostonin Massachusetts Institute of Technology lastenklinikan tietoaineistoa (CHB-MIT). Tämän työn tutkimus sisältää kolme koeryhmää, jotka eroavat toisistaan esikäsittelyvaiheiden osalta: aikasarja-EEG-data esikäsiteltiin perinteisten suodattimien ja normalisointitekniikoiden avulla, ja näin esikäsitelty signaali segmentoitiin epookkeihin. Kukin koeryhmä sisältää 3–4 koetta, jotka eroavat menetelmiltään ja tavoitteiltaan. Kussakin niistä epookkeihin jaettu aikasarjadata muutettiin syötesignaalien erilaisiksi esitysmuodoiksi. Tässä tutkimuksessa tutkittiin ja verrattiin keskenään EEG-signaalia sellaisenaan, EEG-signaalin amplitudi-spektrogrammeja, 1D-FFT-, 2D-FFT-, 2D-FFT-amplitudi- ja 2D-FFT -vaihespektriä. Näin saatuja aika- ja taajuusalueen signaaleja käytettiin erikseen VGG- tai DenseNet 1D -mallien syötetietoina. Paras tulos saatiin VGG-mallilla kun syötetietona oli amplitudi-spektrogrammi ja tällöin tarkkuus oli 0,98, herkkyys 0,71 ja spesifisyys 0,99 henkilöstä riippuvaisella EEG-datalla. VGG yhdessä amplitudi-spektrogrammien kanssa tuottivat lupaavia tuloksia henkilökohtaisen epilepsiakohtausdetektorin rakentamiselle. VGG- ja DenseNet 1D -malleille ei ollut tarpeeksi EEG-dataa henkilöstä riippumattoman luokittelijan opettamiseksi

Automated Detection and Mitigation of Inefficient Visual Searching Using Electroencephalography and Machine Learning

Decisions made during the high-stress and fast-paced operations of the military are extremely prone to cognitive biases. A commonly known cognitive bias is a confirmation bias, or the inappropriate bolstering of an unknown hypothesis. One such critical military operation that can fall prey to a confirmation bias is a visual search. During a visual search, a military operator must perform a visual scan of an environment for a specific target. However, the visual search process can fall prey to the same confirmation bias which can cause inefficient searches. This study elicits inefficient visual search patterns and applies various mitigation techniques in an effort to improve the efficiency of the searches. The effects of the various mitigations are studied and the most effective mitigations are determined. Machine learning models are trained to find the relationship between Electroencephalography (EEG) signals and inefficient visual searching