ABSTRACT To investigate the processing of speech in the brain, commonly simple linear models are used to establish a relationship between brain signals and speech features. However, these linear models are ill-equipped to model a highly-dynamic, complex non-linear system like the brain, and they often require a substantial amount of subject-specific training data. This work introduces a novel speech decoder architecture: the Very Large Augmented Auditory Inference (VLAAI) network. The VLAAI network outperformed state-of-the-art subject-independent models (median Pearson correlation of 0.19, p < 0.001), yielding an increase over the well-established linear model by 52%. Using ablation techniques we identified the relative importance of each part of the VLAAI network and found that the non-linear components and output context module influenced model performance the most (10% relative performance increase). Subsequently, the VLAAI network was evaluated on a holdout dataset of 26 subjects and publicly available unseen dataset to test generalization for unseen subjects and stimuli. No significant difference was found between the holdout subjects and the default test set, and only a small difference between the default test set and the public dataset was found. Compared to the baseline models, the VLAAI network still significantly outperformed all baseline models on the public dataset. We evaluated the effect of training set size by training the VLAAI network on data from 1 up to 80 subjects and evaluated on 26 holdout subjects, revealing a logarithmic relationship between the number of subjects in the training set and the performance on unseen subjects. Finally, the subject-independent VLAAI network was fine-tuned for 26 holdout subjects to obtain subject-specific VLAAI models. With 5 minutes of data or more, a significant performance improvement was found, up to 34% (from 0.18 to 0.25 median Pearson correlation) with regards to the subject-independent VLAAI network.

human cognitive or sensory-motor functions. Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI), is a direct communication link between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Due to the cortical plasticity of the brain, signa In 2006, Sony patented a neural interface system allowing radio waves to affect signals in the neural cortex. In 2007, NeuroSky released the first affordable consumer based EEG along with the game NeuroBoy. It was the first large scale EEG device to use dry sensor technology. In 2008, OCZ Technology developed a device for use in video games relying primarily on electromyography. In 2008, Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create Judecca, a game. In 2009, Mattel partnered with NeuroSky to release Mindflex, a game that used an EEG to steer a ball through an obstacle course. It was by far the best selling consumer based EEG at the time. In 2009, Uncle Milton Industries partnered with NeuroSky to release the Star Wars Force Trainer, a game designed to create the illusion of possessing the Force. In 2009, Emotiv released the EPOC, a 14 channel EEG device that can read 4 mental states, 13 conscious states, facial expressions, and head movements. The EPOC was the first commercial BCI to use dry sensor technology, which can be dampened with a saline solution for a better connection. In November 2011, Time magazine selected "necomimi" produced by Neurowear as one of the year's best inventions. In 2013, g.tec introduced the Unicorn Hybrid Black, a low-cost, portable EEG system designed for research, education, and BCI prototyping. The device combines dry and gel-based electrod Systems that are based on deliberate user control often focus on accuracy and system responsiveness, while passive systems aim at constant monitoring and minimum user effort. Beyond their classification by physical invasiveness, brain computer interfaces (BCIs) are also classified by the way they function. One such distinction is between active and passive BCIs. Active BCIs require that users consciously modulate their neural activity, such as through the application of motor imagery, mental arithmetic, or focused attention to provide commands to an external system. These systems translate neural patterns into control signals and are used where direct user input is required, such as in moving a cursor, spelling words, or operating robotic arms. In contrast, passive BCIs are not influenced by human intention. Instead, they monitor ongoing brain states continuously. For example, a passive BCI may monitor levels of mental workload, alertness, fatigue or affect, and this information can be used to let computing systems adapt themselves to the user's In 1990, a report was given on a closed loop, bidirectional, adaptive BCI controlling a computer buzzer by an anticipatory brain potential, the Contingent Negative Variation (CNV) potential. The experiment described how an expectation state of the brain, manifested by CNV, used a feedback loop to control the S2 buzzer in the S1-S2-CNV paradigm. The resulting cognitive wave representing the expectation learning in the brain was termed Electroexpectogram (EXG). The CNV brain potential was part of Vidal's 1973 challenge. Studies in the 2010s suggested neural stimulation's potential to restore functional connectivity and associated behaviors through modulation of molecular mechanisms. In November 2020, two participants with amyotrophic lateral sclerosis were able to wirelessly control an operating system to text, email, shop, and bank using direct thought using Stentrode, marking the first time a brain-computer interface was implanted via the patient's blood vessels, eliminating the need for brain surgery. In January 2023, researchers reported no serious adverse events during the first year for all four patients, who could use it to operate computers. In a widely reported experiment, fMRI allowed two users to play Pong in real-time by altering their haemodynamic response or brain blood flow through biofeedback. fMRI measurements of haemodynamic responses in real time have also been used to control robot arms with a seven-second delay between thought and movement. In 2008 research developed in the Advanced Telecommunications Research (ATR) Computational Neuroscience Laboratories in Kyoto, Japan, allowed researchers to reconstruct images from brain signals at a resolution of 10x10 pixels. A 2011 study reported second-by-second reconstruction of videos watched by the study's subjects, from fMRI data. Accuracy is additionally improved by the user's mental rehearsal of the words 'bright' and 'dark' in synchrony with the brightness transitions of the letter's circle. === Brain-to-brain communication === In the 1960s a researcher after training used EEG to create Morse code using alpha waves. On 27 February 2013 Miguel Nicolelis's group at Duke University and IINN-ELS connected the brains of two rats, allowing them to share information, in the first-ever direct brain-to-brain interface. Concerns include issues of accountability and responsibility, such as claims that BCI influence overrides free will and control over actions, inaccurate translation of cognitive intentions, personality changes resulting from deep-brain stimulation, and the blurring of the line between human and machine. Other concerns involve the use of BCIs in advanced interrogation techniques, unauthorized access ("brain hacking"), social stratification through selective enhancement, privacy issues related to mind-reading, tracking and "tagging" systems, and the potential for mind, movement, and emotion control.

Decoding mental states from brain activity in humans | Nature Reviews Neuroscience ## Key Points Understanding whether cognitive and perceptual states can be decoded from brain activity alone is a fundamental question in cognitive neuroscience. It is not only relevant for scientific theories of how information is encoded in the brain, but also has important practical and ethical implications. Non-invasive techniques such as functional MRI (fMRI) can be used to record signals related to brain activity in humans from many locations in the brain simultaneously. However, many conventional approaches to analysing these data rely on considering signal changes at each location independently of all the other locations in the brain These conventional approaches have proven successful in elucidating many aspects of the relationship between cognitive and mental states and brain activity. However, recent advances in data analysis procedures raise the possibility of deciphering additional and complementary information from neuroimaging data. Recently, a powerful approach has emerged that applies pattern-recognition techniques to neuroimaging data. The new strategy is to decode a person's