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Presentation 2 with litreature - Coggle Diagram

- - - - Patients with blindsight are blind due to an early visual cortical lesion, but they can discriminate stimuli presented to the blind visual field better than chance. Studies using transcranial magnetic stimulation (TMS) of early visual cortex have tried to induce blindsight-like behaviour in neurologically healthy individuals, but the studies have yielded varied results. We hypothesized that previous demonstrations of TMS-induced blindsight may result from degraded awareness of the stimuli due to the use of dichotomous visibility scales in measuring awareness. In the present study, TMS was applied to early visual cortex during an orientation discrimination task and the subjective scale measuring awareness was manipulated: The participants reported their conscious perception either using a dichotomous scale or a 4-point Perceptual Awareness Scale. Although the results with the dichotomous scale replicated previous reports of blindsight-like behaviour, there was no evidence of TMS-induced blindsight for orientation when the participants used the lowest rating of the 4-point graded scale to indicate that they were not aware of the presence of the stimulus. Moreover, signal detection analyses indicated that across participants, the individual’s sensitivity to consciously discriminate orientation predicted behaviour on reportedly unconscious trials. These results suggest that blindsight-like discrimination of orientation in neurologically healthy individuals does not occur for completely invisible stimuli, that is, when the observers do not report any kind of consciousness of the stimulus. TMS-induced blindsight for orientation is likely degraded conscious vision.
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      - Researchers at the University of Texas at Austin developed the system in part by using a transformer model, which is similar to those that support Google’s chatbot Bard and OpenAI’s chatbot ChatGPT.
        The study’s participants trained the decoder by listening to several hours of podcasts within an fMRI scanner.
        Once the AI system is trained, it can generate a stream of text when the participant is listening to or imagines telling a new story. The resultant text is not an exact transcript, rather the researchers designed it with the intent of capturing general thoughts or ideas.
        Participants first listen to podcasts for about 16 hours in an fMRI scanner. This trains the AI to learn the person’s brain activity. Later, they go back in to put their brain to the test. And tell the same story without actually saying anything out loud. Since we already knew which words they were gonna say, approximately, and when those words would occur, we could then compare that to the actual words and see how well we were doing in decoding
        it works, but it’s hard to really call it accurate because it doesn't turn thoughts into text, word for word.
        It cannot be used on just a random person (without collecting all this training data)
        The decoder can’t be used outside of a laboratory setting because it relies on the fMRI scanner. But the researchers believe it could eventually be used via more portable brain-imaging systems. But the researchers think this work could transfer to other, more portable brain-imaging systems, such as functional near-infrared spectroscopy (fNIRS). (fNIRS measures where there’s more or less blood flow in the brain at different points in time, which, it turns out, is exactly the same kind of signal that fMRI is measuring, resolution will be lower)
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      - A novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations.
        Additionally, the method further leverages the CLIP image encoder to provide extra supervision to better align EEG, text, and image embeddings with limited EEG-image pairs. Overall, the proposed method overcomes the challenges of using EEG signals for image generation, such as noise, limited information, and individual differences, and achieves promising results.
        Quantitative and qualitative results demonstrate the effectiveness of the proposed method as a significant step towards portable and low-cost ``thoughts-to-image'', with potential applications in neuroscience and computer vision.
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      - These days, the Internet of things (IoT) research is driving large-scale development and deployment of many innovative applications. IoT has indeed brought many smart applications to the doorstep of users. IoT has also made it possible to connect many sensors and control equipment. Here, the authors address an important application for physically challenged. The authors present a brain–computer interface (BCI) system to lock/unlock a wheelchair and control its movements using BCI. The approach presented here uses NeuroSky's MindWave Mobile, a single electrode electroencephalography (EEG) headset that can be connected to any Bluetooth-enabled system. The raw EEG data from the headset is processed on an Android mobile device to extract the electromyography (EMG) patterns that occur due to eye blinks and activity of muscles in the jaw. These patterns are used to control the movement of a wheelchair in all possible directions. A biometric security system is provided to lock and unlock the wheelchair by extracting the information about different brain waves from the raw EEG signal. In this system, only the user knows the password which is generated using brain waves and it can lock/unlock the wheelchair and control it. The proposed system was verified and evaluated using a prototype.
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      - This paper investigates the development of an intelligent system method to address completely locked-in-syndrome (CLIS) that is caused by some illnesses such as Amyotrophic Lateral Sclerosis (ALS) as the most predominant type of Motor Neuron Disease (MND). In the last stages of ALS and despite the limitations in body movements, patients however will have a fully functional brain and cognitive capabilities and able to feel pain but fail to communicate. This paper aims to address the CLIS problem by utilizing EEG signals that human brain generates when thinking about a specific feeling or imagination as a way to communicate. The aim is to develop a low-cost and affordable system for patients to use to communicate with carers and family members. In this paper, the novel implementation of the ASPS (Automated Sensor and Signal Processing Selection) approach for feature extraction of EEG is presented to select the most suitable Sensory Characteristic Features (SCFs) to detect human thoughts and imaginations. Artificial Neural Networks (ANN) are used to verify the results. The findings show that EEG signals are able to capture imagination information that can be used as a means of communication; and the ASPS approach allows the selection of the most important features for reliable communication. This paper explains the implementation and validation of ASPS approach in brain signal classification for bespoke arrangement. Hence, future work will present the results of relatively high number of volunteers, sensors and signal processing methods.
    - - Visually impaired and differently abled people who can’t use their hands have to depend on a helper when they wish to use a computer or a smartphone and they wish to enter any data. Depending on someone also causes privacy and security threats as all information including confidential information like passwords,etc also have to be shared so that the helper can write the data into the device. Think2Type is a BCI solution which will translate a user's active intent to text represented by morse code. BCI or ‘Brain Computer Interface’ is a technology that enables its users to interact with computers by means of brainactivity only, this activity being generally measured by ElectroEncephaloGraphy (EEG). Our solution helps them to be more independent as they merely have to think about the characters in the password and use active intent to enter them. The intents correspond to 'dot' and 'dash' of Morse code representation of alpha numerics. It will be converted to the corresponding text and provide them with complete confidentiality.
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      - Attention Deficit Hyperactivity Disorder (ADHD) is a common and heritable disease that has an environmental influence on brain function. The diseases affects multiple aspects of the lives of college students, not only on their study but also on the relationships with other people. The problem with ADHD attention involves short term memory. The purpose of this paper is to investigate the capability of improving short term working memory for ADHD patients by the aid of technology a proper VR environment is built for ADHD, who are isolated from the real circumference. Electroencephalography (EEG) is taken as biofeedback to read the brain signal from the patient. A deep learning approach and an artificial neural network method, are employed to efficiently and accurately process EEG. The findings of the trial indicate that the virtual reality recommended system will play a greater role in improving the attention to the ADHD patient.