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Artificial Neural Networks are Reconstructing Human Thoughts in Realtime

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Earlier this year, researchers from Russia's Neurobotics Corporation and a team at the Moscow Institute of Physics and Technology worked out how to visualize human brain activity by mimicking images observed in real-time. This breakthrough in artificial neural network technology usage will eventually enable post-stroke rehabilitation devices that will be controlled by signals from the brain. The team uploaded their research via a'preprint' on the bioRxiv website and also shared a video that showcased their'mind-reading' device at work. To develop devices that can be controlled by the human and treatments for cognitive disorders or post-stroke rehabilitation, neurobiologists must have an understanding of how the brain encodes data and information. A critical development in the creation of these technologies is the ability to study brain activity using visual perception as a marker.


A Beginner's Guide to Brain-Computer Interface and Convolutional Neural Networks

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Can the mind connect directly with artificial intelligence, robots and other minds through brain-computer interface (BCI) technologies to transcend our human limitations? For some, it is a necessity to our survival. Indeed, we would need to become cyborgs to be relevant in an artificial intelligence age. Brain-Computer Interface (BCI): devices that enable its users to interact with computers by mean of brain-activity only, this activity being generally measured by ElectroEncephaloGraphy (EEG). Electroencephalography (EEG): physiological method of choice to record the electrical activity generated by the brain via electrodes placed on the scalp surface. Functional magnetic resonance imaging (fMRI): measures brain activity by detecting changes associated with blood flow.


Novices 'Download' Pilots' Brainwaves, Learn To Fly

AITopics Original Links

For the first time, scientists have found that stimulating the brain could accelerate learning. Matthew Phillips and his team from Malibu, Calif.-based HRL Information and System Sciences Laboratory used a brain-computer interface called transcranial direct current stimulation to transmit the recorded brainwave patterns from six commercial and military pilots to novices learning to fly. The novices received the brainwaves from a rubber cap embedded with electrodes. These non-invasive electroencephalography (EEG) caps are commonly used in brainwave research and are able to detect and transmit brainwave activity through the skull. Phillips and his team recruited thirty-two right-handed people for the study.


A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction

arXiv.org Artificial Intelligence

This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm is applied to select the most informative features from an initial set of 2550 EEG statistical features. Optimisation of a Multilayer Perceptron (MLP) is performed with an evolutionary approach before classification to estimate the best hyperparameters of the network. Deep learning and tuning with Long Short-Term Memory (LSTM) are also explored, and Adaptive Boosting of the two types of models is tested for each problem. Three experiments are provided for comparison using different classifiers: one for attention state classification, one for emotional sentiment classification, and a third experiment in which the goal is to guess the number a subject is thinking of. The obtained results show that an Adaptive Boosted LSTM can achieve an accuracy of 84.44%, 97.06%, and 9.94% on the attentional, emotional, and number datasets, respectively. An evolutionary-optimised MLP achieves results close to the Adaptive Boosted LSTM for the two first experiments and significantly higher for the number-guessing experiment with an Adaptive Boosted DEvo MLP reaching 31.35%, while being significantly quicker to train and classify. In particular, the accuracy of the nonboosted DEvo MLP was of 79.81%, 96.11%, and 27.07% in the same benchmarks. Two datasets for the experiments were gathered using a Muse EEG headband with four electrodes corresponding to TP9, AF7, AF8, and TP10 locations of the international EEG placement standard. The EEG MindBigData digits dataset was gathered from the TP9, FP1, FP2, and TP10 locations.


Neural network reconstructs human thoughts from brain waves in real time

#artificialintelligence

Researchers from Russian corporation Neurobotics and the Moscow Institute of Physics and Technology have found a way to visualize a person's brain activity as actual images mimicking what they observe in real time. This will enable new post-stroke rehabilitation devices controlled by brain signals. The team published its research as a preprint on bioRxiv and posted a video online showing their "mind-reading" system at work. To develop devices controlled by the brain and methods for cognitive disorder treatment and post-stroke rehabilitation, neurobiologists need to understand how the brain encodes information. A key aspect of this is studying the brain activity of people perceiving visual information, for example, while watching a video.