Could neurolecturing address the limitations of live and recorded lectures?

Lectures are a common teaching method in higher education. However, they have many serious limitations, including boredom, attendance, short attention span, low knowledge transmission and the passivity of students. This paper suggests how a combination of electroencephalography (EEG) and eye-tracking technology could address some of these limitations – an approach that I have called neurolecturing. Neurolecturing could measure students’ attention, learning and cognitive load and provide real time feedback to students and lecturers. It could also play a role in the flipped classroom and artificial intelligence tutoring

Could neurolecturing address the limitations of live and recorded lectures?

Lectures are a common teaching method in higher education. However, they have many serious limitations, including boredom, attendance, short attention span, low knowledge transmission and the passivity of students. This paper suggests how a combination of electroencephalography (EEG) and eye-tracking technology could address some of these limitations – an approach that I have called neurolecturing. Neurolecturing could measure students’ attention, learning and cognitive load and provide real time feedback to students and lecturers. It could also play a role in the flipped classroom and artificial intelligence tutoring

High-wearable EEG-based distraction detection in motor rehabilitation

A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness

Hybrid Mean Fuzzy Approach For Attention Detection

Statistics around the world showed that attention deficit significantly leads to road accidents. Hence, the growth of studies on attention deficit detection becoming more important. The studies obtained the waveform from electroencephalography (EEG) to identify the characteristic of attention. However, each individual has own unique characteristics to significantly shown the attention deficit. Thus, this research aim is to use the fuzzy approach to minimize the variability gap of the EEG signal between each individual. The research conducted the prior experiment to develop control parameter for training set of fuzzy by using two distinct stimulations to create two groups of attention sample i.e., attentive and inattentive. An approach of novel Hybrid Mean Fuzzy (HMF) was proposed in this research to detect attention deficit in EEG signal. It is the combination of simple averaging (Mean) and Fuzzy approaches for EEG analysis and classification. The results of using this method shows a significantly change in EEG signal which correlates to the attention detection. An Attention Degradation Scale (ADS) is successfully developed as the threshold value of EEG for attention detection. Therefore, the findings in this research can be a promising foundation on attention deficit detection in large application not only for reducing the road accidents

Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies

Electroencephalography (EEG)-based Brain-Computer Interfaces

International audienceBrain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field

Riabilitazione motoria attraverso l'uso di un esoscheletro robotico, per pazienti con monoplegia ad una gamba

L’ Ictus Cerebrale è una patologia del cervello che produce una riduzione di nutrimento e di ossigeno al tessuto cerebrale colpito, e quindi alla morte di esso; ciò può provocare vari tipi di invalidità, reversibili o irreversibili, fino alla morte del soggetto colpito. Per una persona colpita da Ictus Cerebrale è fondamentale eseguire un ottimo percorso riabilitativo, per riuscire a recuperare in parte o totalmente la propria autonomia vitale; con questa tesi mi focalizzerò sulla riabilitazione motoria di soggetti che soffrono di monoplegia ad una gamba a seguito di un Ictus. In particolare ho analizzato due tipologie riabilitative: quella muscolare e quella neuro cognitiva. La prima è legata ad una riattivazione del tono muscolare: ho così progettato un esoscheletro robotico a tre braccia che, applicato alla gamba paralizzata, induce nel paziente in maniera automatizzata una mobilitazione passiva dell’arto in modo da favorirne gradualmente il recupero muscolare, in maniera simile a quanto fanno i fisioterapisti manualmente. Inizialmente ho realizzato un modello dinamico da applicare all’esoscheletro per muoverlo; successivamente ho sviluppato un sistema per controllarlo che si basa su un processo di mirroring della gamba non paralizzata, consentendo così di riprodurre la camminata propria del paziente: in questo modo ho un controllo adattivo in cui sarà il paziente stesso a stabilire il suo passo, consentendogli così di imparare nuovamente a camminare in maniera più naturale. La seconda, la riabilitazione neuro cognitiva, afferma che è possibile recuperare da una paralisi motoria ripristinando i processi cognitivi associati all’andatura, presenti nel lobo frontale e andati persi a seguito dell’Ictus. Con tale tesi ho sviluppato un modello matematico che converte un segnale EEG, prelevato dal lobo frontale tramite un apposito sensore, in un segnale che indica il livello di concentrazione del soggetto, il quale sarà usato come una sorta di interruttore On/Off per l’esoscheletro: così per far muovere l’esoscheletro il paziente dovrà concentrarsi mentalmente sulla camminata, ripristinando così tutti i relativi processi cognitivi

Learning EEG-based spectral-spatial patterns for attention level measurement

Abstract — In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person’s level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc. In this paper we propose a novel approach to extract, select and learn spectral-spatial patterns from electroencephalogram (EEG) recordings. Our approach improves over prior-art methods that was, typically, only concerned with power of specific EEG rhythms from few individual channels. In this new approach, spectral-spatial features from multichannel EEG are extracted by a two filtering stages: a filter-bank (FB) and common spatial patterns (CSP) filters. The most important features are selected by a Mutual Information (MI) based feature selection procedure and then classified using Fisher linear discriminant (FLD). The outcome is a measure of the attention level. An experimental study was conducted with 5 healthy young male subjects with their EEG recorded in various attention and non-attention conditions (opened eyes, closed eyes, reading, counting, relaxing, etc.). EEGs were used to train and evaluate the model using 4x4fold cross-validation procedure. Results indicate that the new proposed approach outperforms the prior-art methods and can achieve up to 89.4 % classification accuracy rate (with an average improvement of up to 16%). We demonstrate its application with a two-players attention-based racing car computer game. I