Artificial Intelligence

Unlocking the mind: Why and how AI can help decode brain activity

26th May 2024
Sheryl Miles

Decoding brain activity lies in a fascinating intersection between neuroscience and machine learning (ML).

By Isabel Maranhão, PhD, Neuroscience Growth at Bayezian

When carried out correctly, it can open doors to therapies for many neurological disorders. There are profound benefits for deciphering neural signals and a myriad of ways in which this can enrich our understanding of the human mind and foster clinical benefits. At the heart of achieving this remarkable feat is the helping hand of artificial intelligence.

With so many possibilities, it is hard not to wonder what else can be done and how brain decoding techniques can revolutionise the treatment of neurological disorders.

Why do we want to decode neural activity?

With the evolution of AI, decoding brain activity has become an attainable goal. It has numerous clinical applications, including diagnosing neurological disorders, monitoring brain health and developing personalised treatments based on brain activity patterns.

AI and ML have already helped neuroscience in numerous ways. Scientists can now use ML to diagnose Alzheimer seven years early as well as modelling the progression of the disease given the individual’s neural activity pattern. Brain machine interfaces can then be used to treat Alzheimer’s main symptoms of memory loss.

We can also now detect gender differences in the brain – a big step in personalised medicine – and predict the success of different antidepressant treatments within a week of collecting neural data.

Brain decoding is also beneficial in the treatment of affective disorders such as depression and anxiety. Algorithms can identify unique patterns of brain activity associated with these mental health conditions. These biomarkers can help differentiate between different subtypes of depression, predict treatment response, and monitor disease progression over time, leading to more personalised and effective treatment strategies.

The neural activity can be recorded using invasive and non-invasive methods. But since the former requires going down the surgery route, the latter is receiving a lot of attention for clinical settings. Electroencephalography (EEG) is a non-invasive technique that records the electrical activity of the brain using electrodes placed on the scalp.

The integration of AI with EEG technology represents a promising approach for improving the diagnosis and management of many neurological conditions, and this is particularly evident in the case of epilepsy.

Merging EEG with AI

Epilepsy is a neurological disorder characterised by recurrent seizures, underlined by abnormal bursts of electrical activity in the brain. By analysing EEG signals, clinicians can identify abnormal patterns associated with epilepsy.

ML algorithms can be trained on EEG data from individuals with epilepsy and those without to learn patterns indicative of seizure activity. These algorithms can then be used to automatically analyse EEG recordings and detect abnormal patterns associated with epileptic seizures.

This information enables earlier and more accurate diagnosis, leading to timely interventions and personalised treatment plans. Additionally, decoding brain activity can contribute to the understanding of disease mechanisms, potentially leading to the development of novel diagnostic tools and therapeutic approaches for neurological disorders.

In the case of epilepsy, ML algorithms can be integrated into portable EEG devices, allowing for continuous monitoring of brain activity outside of clinical settings. This enables early detection of incoming seizures, informing the patient so precautions can be taken, ensuring their well-being.

Brain computer interfaces

Another huge application for brain decoding lies within Brain Computer Interfaces (BCIs). A clear clinical impact can be demonstrated with individuals with paralysis, where BCIs translate brain activity into commands that can control external devices, such as computer cursors, robotic arms, or assistive devices.

For individuals with severe paralysis, such as those with spinal cord injuries or neurological disorders like amyotrophic lateral sclerosis (ALS), BCIs offer a means of communication and control that bypasses the need for traditional muscle-based interfaces, offering new possibilities for individuals with paralysis to regain communication and control over their environment.

While the recent surge in interest surrounding BCIs has largely been fuelled by the groundbreaking advancements showcased by Tesla’s Neuralink, it's important to note that the technology itself has been in development for over two decades. What we're witnessing now is not just the continuation of that trajectory, but a pivotal phase where researchers are enhancing the capacity to decipher neural signals, thereby expanding the range of commands that can be effectively communicated through BCIs.

The focus now is to develop applied ML algorithms capable of transfer learning. Transfer learning techniques are applied to EEG nets to leverage pre-trained models on large datasets or related tasks. It involved the algorithm making predictions of labels it was never trained on. This approach helps improve the performance of EEG models, particularly when labelled EEG data is limited or expensive to obtain.

How can brain signals be decoded?

EEG-based neural networks or EEG models utilise various techniques to analyse and interpret EEG data. This includes Convolutional Neural Networks (CNNs), which treat EEG signals as 2D or 3D images and are effective at capturing spatial dependencies in EEG data, making them suitable for tasks such as seizure detection and sleep stage classification.

Another technique is Recurrent Neural Networks (RNNs) – these are utilised to capture temporal dependencies in EEG data. They can model the sequential nature of EEG signals over time, making them suitable for tasks such as EEG-based emotion recognition and cognitive state detection.

Further afield, various deep learning architectures beyond CNNs and RNNs, such as deep belief networks, autoencoders, and hybrid models, can be used to extract hierarchical features from EEG data. These architectures can learn complex representations of EEG signals, enabling tasks such as brain-computer interface control, cognitive workload estimation, and neurofeedback applications.

When it comes to addressing the domain shift between EEG data collected in different settings or populations, domain adaptation techniques are used. By adapting EEG nets from a source domain to a target domain, these techniques improve the generalisation and robustness of EEG models across diverse populations or recording conditions.

Unlocking the mysteries of the mind

AI's ability to analyse complex data and recognise patterns makes it a powerful tool for decoding neural activity, leading to advancements in neuroscience, medicine, and brain-computer interface technology. Thanks to ML techniques on brain activity, AI has already had a profound impact on the diagnosis and treatment of neurological disorders, including other disorders not mentioned in this piece such as Parkinson, strokes, and other traumatic injuries.

Decoding brain activity holds great promise for advancing our understanding of the brain, developing innovative technologies and improving human health and wellbeing. By deciphering the neural code, researchers can unlock the mysteries of the mind and harness the brain's potential for a wide range of applications. Personally, I am incredibly excited to witness how these advancements in brain decoding will positively impact the lives of millions of people and the myriad of clinical applications it may bring about.

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