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We further tried to interpret the phenomenology by localizing relevant EEG channels and frequency bands that play the most significant role in predicting RT. This study shows promise from a computational perspective as well as demonstrates improvement of the prediction performance by a fair margin compared to relevant earlier works. These models are becoming more popular as they can be implemented on field-programmable gate arrays (FPGA) accelerators with high performance. We performed binary and 3-class classification as well as regression using the models. Moreover, we designed and implemented state-of-the-art models based on Fully Connected Neural Network (FCNN) and Convolutional Neural Network (CNN) to learn from the periodogram features and to predict RT. In this study, we used the periodogram of single-trial EEG data as the feature space, which is one of the most well-defined forms of spectral information representation. Īs reactions stimulated during perceptual decision-making vary considerably from trial-to-trial, it is a significant challenge to predict RT based on subtle and intricate information extracted from EEG signals. In addition to this, it can also help monitor people with neuromuscular disorders and in stroke rehabilitation. People with a wide range of language and speech impairments, including congenital impairments such as autism and cerebral palsy as well as acquired conditions such as spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS) can be largely benefited by this. Accurate prediction of RT can help determine lapses in attention or vigilance, the onset of drowsiness or fatigue, decline of motivation, etc., which can help assess human performance, especially in critical tasks such as the air-traffic control or long-haul driving. Thus, by accurately predicting RT, the mental state can be more accurately assessed, yielding a better BCI accuracy. One of the pervasive challenges in BCI research is the reduced BCI accuracy over long sessions due to fluctuations in mental state. Most BCI applications favor EEG as the tool to infer the mental state and to understand communicative intent. Our study not only is useful to understand psychological phenomenology but also helps in the translation of brain signals into machine-comprehensible commands that can facilitate augmentative and alternative communication (AAC) using the brain–computer interface (BCI). This paper focuses on analyzing human response towards visual stimulus and provides methods of estimating RT from information embedded in electroencephalogram (EEG) signals. Accurate prediction of reaction time (RT) using neurophysiological biomarkers can help detect various mental states and develop better human–computer interfaces for patients and healthy subjects. Cognitive and affective state monitoring has become a topic of interest in understanding various sensory-motor functions. Thanks to advancements in sensor and hardware capabilities, and signal processing techniques, we now have better tools to understand the brain. Investigating further, we found that the left central as well as parietal and occipital lobes were crucial for predicting RT, with significant activities in the theta and alpha frequency bands.
![visual testing conjuction vs easy eeg visual testing conjuction vs easy eeg](https://venturebeat.com/wp-content/uploads/2018/07/drone-e1511844719321.jpeg)
The regression-based approach predicted RTs with correlation coefficients (CC) of 0.78 and 0.80 for FCNN and CNN, respectively.
![visual testing conjuction vs easy eeg visual testing conjuction vs easy eeg](https://www.bitbrain.com/sites/default/files/styles/optimized_image/public/eeg-artifacts-filtering-technique-rejection.png)
With the FCNN model, the accuracies obtained for binary and 3-class classification were 93% and 76%, respectively, which further improved with the use of CNN (94% and 78%, respectively). Though deep neural networks are widely known for classification applications, cascading FCNN/CNN with the Random Forest model, we designed a robust regression-based estimator to predict RT. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each trial. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals.