Integrative Neuroscience Research

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
Reach Us +1 (202) 780-3397

Opinion Article - Integrative Neuroscience Research (2024) Volume 7, Issue 2

Eeg and ai: Revolutionizing brain health

Chloe Brown*

Department of Brain & Behavior, University of Sydney, Australia

*Corresponding Author:
Chloe Brown
Department of Brain & Behavior
University of Sydney, Australia.
E-mail: cbro@sydney.edu.au

Received : 04-Jan-2024, Manuscript No. AAINR-24-173; Editor assigned : 08-Jan-2024, PreQC No. AAINR-24-173(PQ); Reviewed : 26-Jan-2024, QC No AAINR-24-173; Revised : 06-Feb-2024, Manuscript No. AAINR-24-173(R); Published : 15-Feb-2024 , DOI : 10.35841/ aainr-7.1.173

Citation: Brown C. Eeg and ai: Revolutionizing brain healt. Integr Neuro Res. 2024;07(02):173.

Visit for more related articles at Integrative Neuroscience Research

Introduction

The field of Electroencephalography (EEG) has seen significant evolution, particularly with the integration of sophisticated computational techniques. A key area of development involves deep learning methods applied to EEG-based Brain-Computer Interfaces (BCIs). These methods have shown remarkable potential in improving various aspects of BCI performance, including signal processing, feature extraction, and classification accuracy. Researchers are actively exploring diverse deep learning architectures to enhance BCI capabilities, paving the way for more efficient and reliable human-computer interaction [1].

Beyond BCIs, EEG's diagnostic potential is a major focus. Machine learning applications are being rigorously evaluated for their role in the EEG-based diagnosis of depression. Systematic reviews in this area identify crucial EEG features and algorithms that hold promise for objective and early detection of this condition, while also highlighting the practical challenges and future directions necessary for clinical translation [2].

Similarly, machine learning algorithms are systematically examined for predicting epileptic seizures from EEG signals. This line of inquiry synthesizes findings on various feature extraction methods and classification models, assessing their effectiveness and inherent limitations in achieving reliable seizure prediction [4].

Automated analysis of EEG signals represents another significant advancement, especially in the context of sleep disorder diagnostics. New methods are emerging, such as those leveraging convolutional neural networks directly on raw EEG signals for automated sleep staging. This approach demonstrates improved accuracy and efficiency compared to older, feature-engineered techniques, offering a promising path for practical applications in clinical settings [3].

Understanding and monitoring cognitive and emotional states through EEG is also gaining traction. Comprehensive surveys provide overviews of emotion recognition techniques, detailing various feature extraction methods, classification algorithms, and available datasets. These studies often point out the difficulties in achieving high accuracy and generalizability in real-world emotion recognition applications [5].

Furthermore, systematic reviews and meta-analyses are synthesizing findings on EEG-based methods for assessing cognitive load. They pinpoint consistent EEG markers and specific frequency bands linked to different levels of mental effort, offering insights into the robustness and broad applicability of these measures across diverse tasks [7].

This extends into immersive environments, with systematic reviews investigating the integration of EEG with Virtual Reality (VR) for mental workload monitoring. EEG provides objective metrics of cognitive engagement and stress within VR experiences, which is valuable for developing adaptive VR systems and training protocols [10].

The scope of EEG application also encompasses continuous monitoring and targeted treatment for neurological conditions. Wearable EEG devices represent an emerging field, enabling continuous brain monitoring for various applications, including cognitive state assessment, sleep tracking, and surveillance of neurological disorders. Reviews emphasize the benefits of portability alongside the technical challenges of maintaining signal quality in these innovative devices [6].

For conditions like Autism Spectrum Disorder (ASD), systematic reviews are actively exploring potential EEG biomarkers. They examine differences in brain oscillations, connectivity, and evoked potentials in individuals with ASD compared to neurotypical controls, highlighting markers that could aid in early diagnosis and deepen the understanding of ASD pathophysiology [8].

Moreover, EEG neurofeedback has been evaluated as a treatment for Attention-Deficit/Hyperactivity Disorder (ADHD). Systematic reviews and meta-analyses assess the efficacy of this approach in improving core ADHD symptoms, comparing different neurofeedback protocols and their overall clinical relevance [9].

 

Conclusion

This body of research showcases the expansive utility of Electroencephalography (EEG) integrated with advanced computational methods, particularly machine learning and deep learning. Significant strides are evident in Brain-Computer Interfaces (BCIs), where deep learning improves signal processing and classification accuracy. EEG also proves invaluable for diagnosing various neurological and mental health conditions. For example, machine learning applications are systematically reviewed for diagnosing depression and predicting epileptic seizures, identifying promising features and algorithms for early detection. Beyond diagnosis, EEG plays a crucial role in monitoring and treatment. Automated sleep staging, using convolutional neural networks on raw EEG, demonstrates enhanced accuracy for sleep disorder diagnostics. Furthermore, EEG neurofeedback shows efficacy as a treatment for Attention-Deficit/Hyperactivity Disorder (ADHD), with systematic reviews assessing improvements in core symptoms. The scope extends to emotional states, with surveys on emotion recognition techniques based on EEG signals, and cognitive states, through systematic reviews on assessing cognitive load and mental workload, even within immersive Virtual Reality environments. Wearable EEG devices are also being explored for continuous brain monitoring, underscoring the push for portable and practical applications. Finally, research identifies potential EEG biomarkers for Autism Spectrum Disorder, aiming for earlier diagnosis and a deeper understanding of its pathophysiology. These studies collectively highlight the transformative impact of computational EEG analysis in both clinical and applied neuroscience.

References

    1. Jingyuan A, Yihui W, Chunqi G. Deep Learning for EEG-Based Brain-Computer Interfaces: A Review. IEEE Trans Hum-Mach Syst. 2022;52(6):1042-1055.

Indexed at, Google Scholar, Crossref

    1. Gholamreza N, Seyedmostafa R, Navid S. Machine learning with EEG for depression diagnosis: A systematic review. J Neurosci Methods. 2021;358:109205.

Indexed at, Google Scholar, Crossref

    1. Huu P, Klaus BM, Thorsten H. Automated sleep staging using convolutional neural networks and raw EEG signals. J Neural Eng. 2020;17(1):016007.

Indexed at, Google Scholar, Crossref

    1. Mohamad D, Abdallah T, Abdelhamid SA. A systematic review on seizure prediction using EEG signals and machine learning techniques. J Med Syst. 2021;45(6):66.

Indexed at, Google Scholar, Crossref

    1. Ayman A, Hadi S, Isra A. A Survey on Emotion Recognition Using EEG Signals. IEEE Access. 2019;7:16182-16201.

Indexed at, Google Scholar, Crossref

    1. Shichao S, Doo-Hyun L, Xinhong Z. Wearable EEG for continuous brain monitoring: A systematic review of current applications. Sensors (Basel). 2022;22(15):5698.

Indexed at, Google Scholar, Crossref

    1. Haihua Y, Chunyang Z, Hui L. EEG-based cognitive load assessment: A systematic review and meta-analysis. J Neural Eng. 2020;17(4):043001.

Indexed at, Google Scholar, Crossref

    1. Jing W, Xiaodan T, Xiaohong L. EEG biomarkers in autism spectrum disorder: A systematic review. Clin Neurophysiol. 2020;131(6):1297-1307.

Indexed at, Google Scholar, Crossref

    1. Jelle VD, Martijn A, Hartmut H. EEG neurofeedback for attention-deficit/hyperactivity disorder (ADHD): A systematic review and meta-analysis. Clin EEG Neurosci. 2019;50(1):3-15.

Indexed at, Google Scholar, Crossref

    1. Shuang Y, Fei M, Jing Z. EEG-Based Monitoring of Mental Workload in Virtual Reality: A Systematic Review. Sensors (Basel). 2023;23(1):228.

Indexed at, Google Scholar, Crossref

Get the App