Artificial neural networks (ANN)

Definition

Artificial neural networks (ANN) are computing systems inspired by the biological neural networks that constitute animal brains. They consist of interconnected groups of artificial neurons that process information using a connectionist approach to computation, allowing them to learn from data. This capability enables them to identify patterns and make decisions, which is particularly beneficial in applications like brain-computer interfaces (BCIs), where they can enhance signal interpretation and user interaction.

5 Must Know Facts For Your Next Test

1. ANNs are capable of approximating complex functions and can generalize from examples, making them powerful tools for tasks like classification and regression.
2. In SSVEP-based BCIs, ANNs can effectively classify brain signals that are triggered by visual stimuli presented at specific frequencies, leading to enhanced user control.
3. For SMR-based BCIs, ANNs help interpret the brain's sensorimotor rhythms, enabling applications such as controlling prosthetic devices or computer interfaces through thought alone.
4. ANNs in spelling and communication systems utilize patterns from brain signals to predict user intent, significantly improving communication speed and accuracy for individuals with disabilities.
5. Deep learning, a subset of ANN, employs multiple layers of neurons, allowing for more sophisticated feature extraction and better performance on complex BCI tasks.

Review Questions

• How do artificial neural networks contribute to the interpretation of SSVEP signals in brain-computer interfaces?
  • Artificial neural networks enhance the interpretation of steady-state visual evoked potential (SSVEP) signals by classifying brain activity related to specific visual stimuli. By training on data collected during these tasks, ANNs learn to recognize patterns associated with different frequencies of visual input. This ability allows users to control devices more effectively and improves the reliability of BCI systems based on SSVEP.
• What role do artificial neural networks play in enhancing the functionality of SMR-based BCIs?
  • Artificial neural networks significantly improve the functionality of sensorimotor rhythm (SMR)-based BCIs by accurately interpreting and classifying EEG signals associated with motor imagery. Through training, ANNs can distinguish between different motor commands based on the brain's rhythmic activity patterns. This capability enables users to operate assistive devices or prosthetics more intuitively and efficiently, transforming their ability to interact with technology.
• Evaluate the impact of artificial neural networks on the development of spelling and communication systems for individuals with disabilities.
  • The integration of artificial neural networks into spelling and communication systems has profoundly impacted how individuals with disabilities interact and communicate. By leveraging ANNs to analyze brain signals, these systems can predict user intentions more accurately and quickly than traditional methods. This advancement not only increases communication efficiency but also enhances user experience by providing a more responsive interface tailored to individual needs, ultimately fostering greater independence.

Related terms

Neurons: Basic units in artificial neural networks that process input data and produce output signals, similar to biological neurons.

Training: The process of teaching an artificial neural network by exposing it to a dataset, allowing it to adjust its parameters to improve performance on tasks.

Activation Function: Mathematical functions applied to the output of neurons in an ANN, determining whether a neuron should be activated based on the input received.