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Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
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Historic Events and Development in Brain Computer Interface over 50 years

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Dr. Rishabh Pathak


 

The history and development of Brain-Computer Interfaces (BCIs) span over fifty years, highlighting significant milestones that have shaped the field.

Early Foundations (1920s - 1970s)

1.      1924 - First EEG Recording:

Hans Berger was the first to record human brain activity using electroencephalography (EEG). His work led to the identification of brain wave patterns, such as alpha and beta waves, laying the groundwork for future BCI development.

2.     1930s - Electrocorticography Development:

W. Penfield and Herbert Jasper pioneered the use of electrocorticography (ECoG) for detecting epileptic foci, introducing invasive techniques for measuring brain signals directly from the surface of the brain.

3.     1960s - Initial BCI Concepts:

Research on direct brain control of external devices began to emerge, signaling the initial conceptual development of BCIs. Researchers started exploring how signals from the brain could be translated into commands for computers or prosthetic devices.

4.    1970s - Neuromuscular Control:

The first applications of BCI involved neural signals to control external devices, like moving cursors on a screen, mainly using invasive methods.

Technological Advancements and Applications (1980s - 1990s)

5.     1980s - Emerging Non-Invasive Techniques:

The introduction of non-invasive techniques, primarily EEG-based BCIs, gained traction. These methods were acclaimed for their ability to record brain activity without surgical intervention, making them more acceptable for research and clinical settings.

6.    1990 - First Successful BCI System:

A significant breakthrough occurred when a patient with severe motor impairments was able to control a computer cursor using only brain signals. This marked the first real-world application of a BCI system, demonstrating the potential for communication and control through brain activity.

Expansion and Research Growth (2000s)

7.     Early 2000s - Commercialization Efforts:

Research institutions and companies began developing commercial BCI systems tailored for rehabilitation and assistive technologies, such as controlling prosthetic limbs and communication devices for paralyzed individuals.

8.    2004 - BrainGate System:

The BrainGate project exemplified cutting-edge BCI technology, allowing patients with spinal cord injuries to control computer cursors using ECoG signals. This system demonstrated the capability of high-fidelity brain signal processing in real-time applications.

9.    2006 - Increase in Popular Research:

Advances in machine learning and signal processing significantly enhanced the accuracy of BCI systems. This period also saw increased collaboration between engineering, neuroscience, and clinical research fields.

Recent Developments and Future Directions (2010 - Present)

10.                        2010-2020 - High-Density EEG Systems:

The advent of high-density EEG technologies improved spatial resolution and signal quality. Researchers began using these systems for various applications, including emotions and cognitive state monitoring.

11.  2015 - Advancements in Invasive BCIs:

Ongoing research in clinical trials showcased improvements in invasive techniques. For instance, patients with paralysis regained the ability to control robotic arms through direct cortical stimulation techniques that offered more dexterous movements.

12. 2019 - Neuralink:

Elon Musk's company, Neuralink, inspired renewed interest in neurotechnology with the aim to develop implantable BCIs that could allow for high-bandwidth communication between humans and computers, paving the way for future applications in treating neurological conditions and enhancing cognitive capabilities.

Current State and Future Outlook

  • Current Applications:

BCIs are being utilized in various fields, including gaming, rehabilitation, education, and communication for individuals with disabilities. Non-invasive methods, particularly EEG, are prevalent due to their accessibility and relative safety.

  • Research Focus:

Ongoing research aims to address challenges such as improving signal quality, enhancing user interfaces, developing better adaptive algorithms, and exploring the ethical implications of BCI technology.

Conclusion

The journey of Brain-Computer Interfaces over the past fifty years has been marked by groundbreaking discoveries, significant technological advancements, and a growing interdisciplinary approach. As research continues to evolve, the potential applications of BCIs expand, promising transformative changes in communication, rehabilitation, and even cognitive enhancements. The future of BCIs holds exciting possibilities, including further integration with artificial intelligence and novel therapeutic applications for various neurological conditions.

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