Auto Train Brain nörogeribildirim ödüllendirme arayüzlerinin etkinlik açısından karşılaştırılması

Background/aim: Auto Train Brain is a mobile app that was specifically developed for dyslexic children to increase their reading speed and reading comprehension. In the original mobile app, only one unique neurofeedback user interface provided visually and audibly rewarding feedback to the subject with a red-green colored arrow on the screen. Later, new modules are added to the app with the end-users requests. These are the “youtube” video-based interface and “Spotify” auditory-based interface. In this research, we have compared the efficacy of the neurofeedback rewarding interfaces. Materials and methods: The experiment group consists of 20 dyslexic children aged 7-to 10 (15 males, 5 females) who were randomly assigned to one rewarding interface and used it at home for more than six months. Results: The result indicates that though the “youtube” interface is liked most by the participants, the arrow-based simple neurofeedback interface reduces theta brain waves more than other rewarding schemes. On the other hand, “youtube” and “Spotify” based interfaces increase Beta band powers more than the arrow interfaces in the cortex. The ”Spotify” user interface improves the fast brain waves more on the temporal lobes (T7 and T8) as the feedback given was only auditory. Conclusion: The results indicate that the relevant neurofeedback rewarding interface should be chosen based on the dyslexic child’s specific condition.Arka plan/amaç: Auto Train Brain, disleksili çocukların okuma hızını ve anlama düzeyini artırmak için özel olarak geliştirilmiş bir mobil uygulamadır. Orijinal mobil uygulamada, yalnızca bir benzersiz nörogeribildirim kullanıcı arayüzü, ekranın kırmızı-yeşil renkli bir oku ile konuya görsel ve işitsel olarak ödüllendirici geri bildirimi sağlıyordu. Daha sonra, kullanıcı talepleriyle uygulamaya yeni modüller eklendi. Bunlar “youtube” video tabanlı arayüz ve “Spotify” işitsel tabanlı arayüzdür. Bu araştırmada, nörogeribildirim ödüllendirme arayüzlerinin etkinliğini karşılaştırdık. Malzemeler ve yöntemler: Deney grubu, 6 ay ve üzeri süreyle evde bir ödüllendirme arayüzü kullanan ve 7 ila 10 yaş arasında (15 erkek, 5 kadın) disleksili çocuktan oluşmaktadır. Sonuçlar: Sonuçlar, “youtube” arayüzünün katılımcılar tarafından en çok beğenilmesine rağmen, ok şeklindeki basit nörogeribildirim arayüzüne göre daha az theta beyin dalgaları azalttığını göstermektedir. Diğer yandan, “youtube” ve “Spotify” tabanlı arayüzler, kortekste ok arayüzlerinden daha fazla beta bant güçlerini artırmaktadır. “Spotify” kullanıcı arayüzü, sadece işitsel olarak verilen geri bildirim nedeniyle temporal loblarda (T7 ve T8) hızlı beyin dalgalarını daha da iyileştirir. Çalışmanın özeti: Sonuçlar, disleksili çocuğun özel durumuna göre ilgili nörogeribildirim ödüllendirme arayüzünün seçilmesi gerektiğini göstermektedir.Publisher's Versio

Electroencephalographic identifiers of reading abilities in turkish language

Fluent reading requires learning the print knowledge of alphabet symbols (letters), rapid automatic naming and phonological awareness skills. In this study, electroencephalo-graphic brain signals of 17 subjects were measured with an eMotiv EPOC+ headset before, during and after a computer-based training session. For the training, distorted letter prints were created by rotating Turkish letters 180 degrees along the y-axis. Using these distorted letters two different texts, each 150 words in length, were created. Subjects were asked to read these texts before and after the training session. We investigated whether there is an improvement in reading speed and a decrease in number of errors due to the computer-based training and whether we can correlate the success of training with any characteristic of any EEG brain signals. Based on our analysis of the EEG data collected throughout the experiment, we observed that the frequency modulation across resting states in the Theta at the Broca Area (F7 and FC5) predicts individual reading performance measures. Even though there exist a variety of studies indicating a relation of Theta band power and learning performance, EEG measurements with eMotiv EPOC+ had not been previously reported with a Turkish alphabet learning task

Improving cognitive functions of dyslexies using multi-sensory learning and EEG neurofeedback

AutoTrainBrain is a neurofeedback and multi-sensory based mobile phone software application, designed in Sabancı University laboratory with the aim of improving the cognitive functions of dyslexic children. It reads electroencephalography (EEG) signals from 14 channels of eMotiv EPOC+ and processes these signals to provide neurofeedback to child for improving the brain signals with visual and auditory cues in real time. AutoTrainBrain software has been applied to a 14-year old dyslexic child, 10 minutes per week for 9 consecutive weeks. The EEG data has been analyzed by using the following three approaches: estimation of single-channel EEG complexity levels (entropy), spectral brain connectivity between two-channels (coherence), single channel relative Alpha band power ratio. Our experimental analysis shows that the proposed brain training system offers improvements based on the measures used in the three approaches mentioned above. This suggests such training may help increase the number of active cortical neurons and improve regional brain connectivity

Auto Train Brain increases the variance of the gamma band sample entropy in the left hemisphere in dyslexia: a pilot study

Auto Train Brain is a mobile app that improves reading speed and reading comprehension in dyslexia. The efficacy of Auto Train Brain was proven with a clinical trial. We have analyzed the long-term training effects of the Auto Train Brain on dyslexic children. We have collected QEEG data from 14 channels from 21 dyslexic children for 100 sessions and calculated the sample entropy in the gamma bands for the left posterior brain (T7, P7, and O1). Although the gamma band values fluctuate and no permanent increase in the gamma band values is detected after Auto Train Brain training at T7, P7, and O1, the variance of gamma band sample entropy increases as the neurofeedback session number increases. We have concluded that the Auto Train Brain increases the flexibility of the left brain in dyslexia.Publisher's Versio

Improving reading abilities in dyslexia with neurofeedback and multi-sensory learning

Developmental dyslexia is a subtype of speci c learning disabilities. There are several methods for improving learning abilities, including neurofeedback and multisensory learning methods. As past work has shown, applying neurofeedback can improve spelling, reading, writing skills, normalizing fear, and anxiety of children with dyslexia. Multi-sensory learning methods utilize hearing (audition), reading (vision), seeing (vision), and touching (tactile/ kinaesthetic) simultaneously and proven to be useful for children with dyslexia. Neurofeedback focuses on normalizing the synaptic connections in the cortex, while multi-sensory learning focuses on using di erent parts of the brain to help with the learning process. Neurofeedback with multi-sensory learning (MSL) experiences in helping people with dyslexia was investigated in this research. Auto Train Brain is multi-sensory learning and neurofeedback based mobile application to improve the cognitive functions of children with dyslexia. It reads qEEG signals from EMOTIV EPOC+ and processes these signals aand provides feedback to a child to improve the brain signals with visual and auditory cues. The major contribution of this thesis is that it presents the rst study that combines neurofeedback with multi-sensory learning principles. Moreover Auto Train Brain has a novel neurofeedback technique from 14- electrode channels. Auto Train Brain was applied to 16 subjects with dyslexia more than 60 times for around 30 minutes. 4 of them also received special education. The control group consisted of 14 subjects with dyslexia (mean age: 8.59) who did not have remedial teaching with Auto Train Brain, but who did continue special education. The TILLS test, which is a new neuropsychological test to diagnose dyslexia, was applied to both groups at the beginning of the experiment and after a 6-month duration from the rst TILLS test. Comparison of the pre- treatment and post-treatment TILLS test results indicate that applying neurofeedback and multi-sensory learning method concurrently is feasible for improving reading abilities of people with dyslexia. Reading comprehension of the experimental group improved more than that of the control group statistically signi cantl

k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia

Learning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.Science Citation Index Expanded (SCI-EXPANDED)WOS:000795733400001PMID: 35575241Affiliation ID: 6001047

Assessing dyslexia with machine learning: a pilot study utilizing Google ML Kit

In this study, we explore the application of Google ML Kit, a machine learning development kit, for dyslexia detection in the Turkish language. We collected face-tracking data from two groups: 49 dyslexic children and 22 typically developing children. Using Google ML Kit and other machine learning algorithms based on eye-tracking data, we compared their performance in dyslexia detection. Our findings reveal that Google ML Kit achieved the highest accuracy among the tested methods. This study underscores the potential of machine learning-based dyslexia detection and its practicality in academic and clinical settings.Publisher's Versio

Can we predict who will respond more to neurofeedback with resting state EEG?

AutoTrainBrain is a neurofeedback and multi sensory learning-based mobile phone software application, designed at Sabanci University with the aim of improving the cognitive functions of dyslexic children. We investigated whether we can predict who will respond more to neurofeedback applied by AutoTrainBrain by analyzing the resting state EEG brain data. Based on our analysis of the EEG data collected, we observed that the power amplitudes across resting states in the theta band over the left Dorsolateral Prefrontal Cortex (DLPFC) (electrode: FC5) predicts who will respond more to neurofeedback with AutoTrainBrain (Pearson correlation coeff: 0.78, P<0.001). When we reduce the high theta brain waves with neurofeedback in this area, we hypothesize that better cortical regulation and inhibition are developed in the brain, therefore the response to neurofeedback increases

Changes in EEG complexity with neurofeedback and multi-sensory learning in children with dyslexia: a multiscale entropy analysis

Multiscale entropy analysis (MSE) is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. MSE has been successfully applied in the literature when measuring autism traits, Alzheimer's, and schizophrenia. However, until now, there has been no research on MSE applied to children with dyslexia. In this study, we have applied MSE analysis to the EEG data of an experimental group consisting of children with dyslexia as well as a control group consisting of typically developing children and compared the results. The experimental group comprised 16 participants with dyslexia who visited Ankara University Medical Faculty Child Neurology Department, and the control group comprised 20 age-matched typically developing children with no reading or writing problems. MSE was calculated for one continuous 60-s epoch for each experimental and control group's EEG session data. The experimental group showed significantly lower complexity at the lowest temporal scale and the medium temporal scales than the typically developing group. Moreover, the experimental group received 60 neurofeedback and multi-sensory learning sessions, each lasting 30 min, with Auto Train Brain. Post-treatment, the experimental group's lower complexity increased to the typically developing group's levels at lower and medium temporal scales in all channels

A mobile app that uses neurofeedback and multi-sensory learning methods improves reading abilities in dyslexia: a pilot study

Reading comprehension is difficult to improve for children with dyslexia because of the continuing demands of orthographic decoding in combination with limited working memory capacity. Children with dyslexia get special education that improves spelling, phonemic and vocabulary awareness, however the latest research indicated that special education does not improve reading comprehension. With the aim of improving reading comprehension, reading speed and all other reading abilities of children with dyslexia, Auto Train Brain that is a novel mobile app using neurofeedback and multi-sensory learning methods was developed. With a clinical study, we wanted to demonstrate the effectiveness of Auto Train Brain on reading abilities. We compared the cognitive improvements obtained with Auto Train Brain with the improvements obtained with special dyslexia training. Auto Train Brain was applied to 16 children with dyslexia 60 times for 30 minutes. The control group consisted of 14 children with dyslexia who did not have remedial training with Auto Train Brain, but who did continue special education. The TILLS test was applied to both the experimental and the control group at the beginning of the experiment and after a 6-month duration from the first TILLS test. Comparison of the pre- and post- TILLS test results indicated that applying neurofeedback and multi-sensory learning method improved reading comprehension of the experimental group more than that of the control group statistically significantly. Both Auto Train Brain and special education improved phonemic awareness and nonword spelling