Can You Control Robot with Your Mind? https://youtu.be/gKFmTCOqe1U
EEG is a brain-computer interface that allows robots to read human thoughts. It's a noninvasive technology, meaning there are no needles. It's also non-destructive because it doesn't require surgery or permanent modifications to the brain (compared to other BCIs).
EEG has been used in medicine for decades, but this technology has recently been applied to robotics. EEGs were initially developed as a way for doctors to study how electrical signals move through wired brains during sleep, seizures, and meditation—and now they're being used by researchers across many different fields who want real-time access into their subjects' thoughts so they can better understand how humans think about things like movement or decision making.
Sensors and Signal Processing
EEG signals are recorded by electrodes placed on the scalp. These electrodes are connected to amplifiers and then digitized by an analog-to-digital converter, which provides a continuous stream of numerical values that digital computers can use.
The EEG signal is very low frequency, so it must be amplified before processing. In order to acquire data from which a robot can learn, we first need to record signals from human subjects.
Feature Extraction
In the context of robotics, feature extraction refers to the process of converting a data set into a new representation.
The features are often numbers or other attributes that describe some aspect of a data set. The algorithm then uses these numbers as input for its final output.
For example, extracting features from an image, such as its color and texture, is often helpful in image processing. These features can then be used by another algorithm that predicts something about the image based on those extracted features (such as "this object is green").
Machine Learning Methods
Machine learning is the art of making computers learn from data. Using a set of algorithms, it can be taught to predict how likely an event is to occur given specific inputs.
This can be done through supervised learning, where you provide the computer with labeled training data; unsupervised learning, where no labeling is needed; or reinforcement learning, where the computer learns by trying out different actions and comparing their outcomes.
In robotics, we use machine learning methods to solve problems that are too difficult or time-consuming for humans to solve manually.
For example: A robot navigating its way through an unknown environment might use various sensors (such as cameras) to build up a map of its surroundings over time. It would then use this map to navigate efficiently through this space while avoiding obstacles along the way - all without any explicit instructions on how best to avoid obstacles!
It's all thanks to deep neural networks, which are trained on millions of examples taken from previous runs by other robots who had successfully avoided obstacles in similar environments before them.
An overview of EEG in robotics
EEG-based BCI, which measures brain activity, is a new technology that enables human mental activities to be transferred to a robot based on electroencephalograph (EEG) signals.
This technology has been used for brain control and robot control. In the field of BRI, Brain Robot Interaction (BRI) devices using EEG signals allow humans to control robots directly through their brainwaves without any physical connection between their brains and the machines they are controlling.
In addition, many studies have related to improving the performance of BCIs by developing new sensors or improving existing ones.
The Brain Research Institute at Boston University has been studying research on brain-computer interfaces. They developed a commercial device called "BrainPort V100" for visually impaired people who cannot see anything due to blindness caused by diseases such as Retinitis Pigmentosa (RP).
This device transmits information about surrounding objects detected by an eye camera through tactile data obtained from electrodes placed on various parts of your face while wearing special glasses equipped with cameras
Conclusion:
The field of machine learning is a rapidly growing one, and there is still much to be explored. The main objective of this article was to provide an overview of EEG in robotics, as well as its use cases and applications.
We need to understand some of the challenges that this technology faces when being used in robots, including signal processing issues such as artifacts and movement artifacts due to eye, blinks, etc., on top of which we mentioned feature extraction methods that can be used for extracting features from EEG signals like amplitude, frequency or phase information.
Other challenges that this technology faces when being used in robots include signal processing issues such as artifacts and movement artifacts due to eye, blinks, etc., on top of which we mentioned feature extraction methods that can be used for extracting features from EEG signals like amplitude, frequency or phase information.
Moreover, we concluded our work by pointing out some possible future directions for research related to this topic, such as using deep neural networks instead.
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