Optimization of Electroencephalograph-Based Classification for Imaginary Motion Brain Computer Interface Study

Using Electroencephalography (EEG) to detect imaginary motions from brain waves, to interface human and computer is a very nascent and challenging field that started developing rapidly in the past few decades. The technique involves some electrodes attached on the scalp of the patient and the signals generated by the brain while the thought process of the patient is captured and recorded in a computer. This technique of human and computer interfacing is termed as Brain Computer Interface (BCI). Disability is a serious problem of our nation and hence BCI is extremely important in case of people who are incapable of communicating due to spinal cord injury. This technique uses the brain signals to make decisions, control objects and communicate with the world using brain integration with peripheral devices and systems. This requires some intelligence to classify these motions. Neural network have been used as a mean to classify motions, however, the accuracy of classification for certain motion was limited. The novelty of the proposed approach is in using a majority vote system for a network of artificial neural networks (ANNs) that is used to optimally classify imaginary motions performed by multiple subjects. Three kinds of imaginary motionswere classified which are imaginary left hand movement, imaginary right hand movement, and imagination of words starting with the same letter. Using an optimized set of electrodes, classification accuracywas optimized for the three users as a group and also individually. The optimization procedure was conducted based on the rank of the electrodes 2 according to their individual classification accuracy, and the eliminating electrodes with the lowest accuracies. The group optimization of 3 subjects altogether resulted in an electrode structure consisting of 15 electrodes with a relatively high classification accuracy of almost 80%. The individual optimization for each subject resulted in an electrode structure of 20 for subject 1 and subject 3 with classification accuracies of 63:63% and 84:33% respectively and single electrode structure for subject 2 with an accuracy of 94:01%. The overall average classification accuracy of all the users with the individual optimization of electrodes was as high as 82:32%

Empowering human-AI teams via Intentional Behavioral Synchrony

As Artificial Intelligence (AI) proliferates across various sectors such as healthcare, transportation, energy, and military applications, the collaboration between human-AI teams is becoming increasingly critical. Understanding the interrelationships between system elements - humans and AI - is vital to achieving the best outcomes within individual team members' capabilities. This is also crucial in designing better AI algorithms and finding favored scenarios for joint AI-human missions that capitalize on the unique capabilities of both elements. In this conceptual study, we introduce Intentional Behavioral Synchrony (IBS) as a synchronization mechanism between humans and AI to set up a trusting relationship without compromising mission goals. IBS aims to create a sense of similarity between AI decisions and human expectations, drawing on psychological concepts that can be integrated into AI algorithms. We also discuss the potential of using multimodal fusion to set up a feedback loop between the two partners. Our aim with this work is to start a research trend centered on exploring innovative ways of deploying synchrony between teams of non-human members. Our goal is to foster a better sense of collaboration and trust between humans and AI, resulting in more effective joint missions

An organelle-specific protein landscape identifies novel diseases and molecular mechanisms

Contains fulltext : 158967.pdf (publisher's version ) (Open Access)Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine

TCTEX1D2 mutations underlie Jeune asphyxiating thoracic dystrophy with impaired retrograde intraflagellar transport

Tiina Paunio on työryhmän UK10K jäsen.The analysis of individuals with ciliary chondrodysplasias can shed light on sensitive mechanisms controlling ciliogenesis and cell signalling that are essential to embryonic development and survival. Here we identify TCTEX1D2 mutations causing Jeune asphyxiating thoracic dystrophy with partially penetrant inheritance. Loss of TCTEX1D2 impairs retrograde intraflagellar transport (IFT) in humans and the protist Chlamydomonas, accompanied by destabilization of the retrograde IFT dynein motor. We thus define TCTEX1D2 as an integral component of the evolutionarily conserved retrograde IFT machinery. In complex with several IFT dynein light chains, it is required for correct vertebrate skeletal formation but may be functionally redundant under certain conditions.Peer reviewe

Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia.

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.MAK is funded by an NIHR Research Professorship and receives funding from the Wellcome Trust, Great Ormond Street Children's Hospital Charity, and Rosetrees Trust. E.M. received funding from the Rosetrees Trust (CD-A53) and Great Ormond Street Hospital Children's Charity. K.G. received funding from Temple Street Foundation. A.M. is funded by Great Ormond Street Hospital, the National Institute for Health Research (NIHR), and Biomedical Research Centre. F.L.R. and D.G. are funded by Cambridge Biomedical Research Centre. K.C. and A.S.J. are funded by NIHR Bioresource for Rare Diseases. The DDD Study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). We acknowledge support from the UK Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy's and St. Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. This research was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. J.H.C. is in receipt of an NIHR Senior Investigator Award. The research team acknowledges the support of the NIHR through the Comprehensive Clinical Research Network. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, Department of Health, or Wellcome Trust. E.R.M. acknowledges support from NIHR Cambridge Biomedical Research Centre, an NIHR Senior Investigator Award, and the University of Cambridge has received salary support in respect of E.R.M. from the NHS in the East of England through the Clinical Academic Reserve. I.E.S. is supported by the National Health and Medical Research Council of Australia (Program Grant and Practitioner Fellowship)

A Multiuser EEG Based Imaginary Motion Classification Using Neural Networks

Using Electroencephalography (EEG) to detect imaginary motions from brain waves to interface human and computer is a very nascent and challenging field that started developing rapidly in the past few decades. This technique is termed as Brain Computer Interface (BCI). BCI is extremely important in case of people who are incapable of communicating due to spinal cord injury. This technique uses the brain signals to make decisions, control and communicate with the world using brain integration with peripheral devices and systems. In this paper, in order to classify imaginary motions, raw data are used to train a system of neural networks with a majority vote output. The network was trained using the scaled conjugate gradient back propagation algorithm. The novelty of this proposed approach is in using a majority vote system for a network of artificial neural networks (ANNs) that is used to optimally classify imaginary motions performed by multiple subjects. EEG data for 3 subjects are used from the BCI Competition III data set V. Each subject has data represented in three sessions comprising of three different types of imaginary motion tasks in each session. Using an optimized set of electrodes, classification accuracy was optimized for the three users as a group. A cross validation method is applied to improve the reliability of the generated results. The optimization resulted in an electrode structure consisting of 15 electrodes with a relatively high classification accuracy of almost 80%. It was observed that using a separate ANN for every channel coupled with a majority vote system was able to improve the average classification accuracy of such imaginary motion of all three users from a maximum 71% to almost 80% while maintaining a relatively simple ANN structure. In addition, the quality of the EEG signal generated by the users declined with time due to fatigue and loss of concentration. It was also concluded that the classification accuracy is user dependent in nature which limits it optimization for multiple subjects. The proposed method presented is novel in the structure of such classification network and in the optimization of channels for multi user EEG-based BCI system

A novel locus for autosomal dominant cone-rod dystrophy in a family of gypsy origin

Póster, 5 figuras, 2 tablas.Cone-rod dystrophy (CORD [MIM #120970]) is a group of genetically and phenotypically heterogenous retinal disorders characterized by primary degeneration of cone photoreceptors, followed by loss of rod photoreceptors (Fig.1). CORD usually manifests in childhood or early adulthood and leads to an early reduction in visual acuity and colour vision, photophobia, sometimes fine nystagmus, and central or paracentral scotomas. In the second stage, due to secondary rod involvement, night blindness becomes apparent, and loss of the visual field extends to the periphery. Genetic heterogeneity of CORD includes autosomal dominant (adCORD), autosomal recessive (arCORD), and X-linked (xlCORD) inheritance. To date, ten genes (AIPL1, CRX, GUCA1A, GUCY2D, PITPNM3, PROM1, PRPH2, RIMS1, SEMA4A and UNC119) and two loci (CORD4 and RCD1) have been identified as responsible for autosomal dominant cone or conerod dystrophy (RetNet).Peer reviewe

Delimitation of the Thoracosphaeraceae (Dinophyceae), Including the Calcareous Dinoflagellates, Based on Large Amounts of Ribosomal RNA Sequence Data

The phylogenetic relationships of the Dinophyceae (Alveolata) are not sufficiently resolved at present. The Thoracosphaeraceae (Peridiniales) are the only group of the Alveolata that include members with calcareous coccoid stages; this trait is considered apomorphic. Although the coccoid stage appar- ently is not calcareous, Bysmatrum has been assigned to the Thoracosphaeraceae based on thecal morphology. We tested the monophyly of the Thoracosphaeraceae using large sets of ribosomal RNA sequence data of the Alveolata including the Dinophyceae. Phylogenetic analyses were performed using Maximum Likelihood and Bayesian approaches. The Thoracosphaeraceae were monophyletic, but included also a number of non-calcareous dinophytes (such as Pentapharsodinium and Pfiesteria) and even parasites (such as Duboscquodinium and Tintinnophagus). Bysmatrum had an isolated and uncertain phylogenetic position outside the Thoracosphaeraceae. The phylogenetic relationships among calcareous dinophytes appear complex, and the assumption of the single origin of the potential to produce calcareous structures is challenged. The application of concatenated ribosomal RNA sequence data may prove promising for phylogenetic reconstructions of the Dinophyceae in future