Computerized Brain Interfaces for Adaptive Learning and Assessment

Arnaldo, Rosa; Iglesias, Javier; Gómez, Victor; Crespo, Javier Lluch; Pérez, Luís Miguel Ruiz; Alonso, José Félix; Sanz, A. Castrillo

doi:10.1007/978-3-319-73888-8_37

Cited by 5 publications

(8 citation statements)

References 12 publications

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“…The results show that combining alpha-gamma did not increase the accuracy of our classification model. According to Arnaldo et al (2018), variations are observed in the alpha and gamma bands during cognitive tasks, such as the visualization of learning tasks. The results of our study identified a methodology to classify visual learners from non-visual learners.…”

Section: Discussionmentioning

confidence: 99%

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Jawed

Amin

Malik³

et al. 2019

Front. Behav. Neurosci.

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This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students’ EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8–10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k -nearest neighbor ( k -NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k -NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k -NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.

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Section: Discussionmentioning

confidence: 99%

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Jawed

Amin

Malik³

et al. 2019

Front. Behav. Neurosci.

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“…Education : Only eight articles were found in this area with two companies. Five articles used Emotiv equipment [ 36 , 37 , 38 , 39 , 40 ] and another three articles used the equipment from InteraXon company [ 41 , 42 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

“…Further, BCIs can be tools for students to increase their learning ability and for educators to further understand their students. A BCI can analyze a student’s cognitive states, assess the information and visualizations to be used, and change the training method to respond to a student’s individual learning needs [ 38 ]. Furthermore, a BCI can help students refocus and increase their interest and engagement in the learning process [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…The details of each research area and the equipment from different companies are: Education: Only eight articles were found in this area with two companies. Five articles used Emotiv equipment [36][37][38][39][40] and another three articles used the equipment from InteraXon company [41][42][43].…”

Section: Rq1: What Are the Publication Trends Based On Eeg Equipment?mentioning

confidence: 99%

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Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Jamil

Belkacem

Ouhbi

et al. 2021

Sensors

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Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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“…A low-cost BCI has been developed, whose characteristics may allow educational institutions to improve the learning and assessment methodologies applied to a particular student [11].…”

mentioning

confidence: 99%

Automatic Exam Evaluation based on Brain Computer Interface

Balat¹,

El-Dosuky²,

El-Razek³

et al. 2020

IJCA

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Brain computer interface applications can be used to overcome learning problems, especially student anxiety, lack of focus, and lack of attention. This paper introduces a system based on brain computer interface (BCI) to be used in education to measure intended learning outcomes and measure the impact of noise on the degree of system accuracy. This system works online and is based on recorded brain signal dataset. The system can be considered as a special case of P300 speller accepting only letters from A to D. These are the possible answers to multiplechoice questions MCQ. The teacher makes exams, stores them in an exam database and delivers them to students. Students enroll into the system and record their brain signals. Brain signals go through preprocessing phase in which signals undergo low and high pass filter. Then the signals undergo a subsampling and segmentation. The features obtained are used as inputs to Linear Discriminant Analysis (LDA). Gained accuracy is 91%.

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Computerized Brain Interfaces for Adaptive Learning and Assessment

Cited by 5 publications

References 12 publications

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Automatic Exam Evaluation based on Brain Computer Interface

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