1. Introduction

Autism Spectrum Disorder, ASD is a complex and extensive neurodevelopmental disorder, which affects patients' daily social interaction, communication skills and some behavior patterns [1]. The diagnosis process of ASD is often a complicated and tedious process. Conventional diagnostic methodologies, notably clinical observation and questionnaire assessments, have historically played pivotal roles in healthcare; however, they are not without their shortcomings. Clinical observation necessitates prolonged monitoring and evaluation by medical professionals, a process that is labor-intensive and susceptible to subjective biases. Meanwhile, questionnaires, often reliant on patient or caregiver reports, are inherently prone to inaccuracies stemming from personal perspectives and recollection biases. Given the escalating prevalence of ASD in recent years, there is a pressing demand for more objective and precise diagnostic instruments capable of facilitating early detection and timely intervention.

Emerging from the technological forefront, Brain Computer Interface (BCI) technologies are revolutionizing the landscape of ASD diagnostics and treatment. Unlike traditional methods, BCI systems directly capture and interpret neural signals, enabling a direct line of communication between the human brain and external devices. This approach offers superior objectivity and real-time monitoring capabilities, providing a direct window into an individual’s neural activity patterns. Scientific inquiry has revealed that individuals with ASD exhibit distinct brain activity signatures during social interactions and communicative exchanges, characterized by atypical synchronization and connectivity patterns within EEG signals. Through sophisticated analysis of these neural signals, BCI systems can discern the unique EEG profiles associated with ASD, thereby enhancing the precision and efficacy of diagnostic procedures. Moreover, BCI facilitates continuous neurological monitoring, offering dynamic insights that inform the development of personalized intervention strategies.

Studies employing EEG signals have successfully differentiated ASD subjects from healthy controls, underscoring the potential of BCI technology in ASD diagnosis. Besides, BCI is being innovatively harnessed in the realm of ASD rehabilitation, where it supports patients in refining their social engagement and communication abilities through immediate feedback mechanisms. This investigation endeavors to explore the potential and practical applications of BCI in the diagnostic framework of ASD [2]. Considering the complexity of ASD and the limitations of conventional diagnostic approaches, such as subjectivity and challenges in early identification, this paper elucidates how BCI technology can offer more objective and detailed neural activity data by directly accessing and interpreting brain activity, thereby underpinning earlier detection and tailored interventions for ASD.

This work highlights the transformative potential of BCI in addressing the diagnostic needs of ASD, emphasizing the technology’s capacity to overcome the inherent constraints of traditional methods. By fostering a more nuanced understanding of the neurological underpinnings of ASD, BCI holds the promise of ushering in a new era of personalized medicine, wherein diagnostic precision and therapeutic effectiveness are significantly enhanced.

2. Clinical features and current landscape of ASD

2.1. Defining characteristics of ASD

ASD manifests through a triad of hallmark traits: deficits in social interaction, impairments in verbal and nonverbal communication, and the presence of repetitive and stereotyped behaviors. Challenges in social interaction are evidenced by difficulties in comprehending emotional cues and intentions of others, coupled with an impaired ability to establish and maintain interpersonal relationships. Verbal and nonverbal communication deficiencies are characterized by struggles in both the production and comprehension of language, with some individuals exhibiting a complete absence of spoken language capability. Repetitive behaviors encompass a range of activities, from the persistent arrangement of objects to an obsessive focus on specific routines or interests.

2.2. Existing diagnostic approaches and their limitations

The diagnostic framework for ASD primarily hinges upon the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM5), alongside a suite of clinical assessment tools. The DSM5, a globally acknowledged criterion for psychiatric disorders, delineates the diagnostic criteria and symptomatics of ASD [3]. Clinical assessment instruments, such as the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview Revised (ADIR), are extensively utilized in the clinical milieu to aid practitioners in evaluating behavioral patterns and symptomatic expressions. Nevertheless, these diagnostic methodologies harbor certain limitations. Firstly, subjectivity is a prevailing concern. The outcomes of clinical observations and evaluations are susceptible to the influence of individual clinician experiences and subjective interpretations, potentially leading to diagnostic inconsistencies. Secondly, early identification poses a significant challenge. The subtlety of early ASD symptoms renders their accurate detection through conventional diagnostic approaches problematic. Lastly, the resource-intensive nature of the diagnostic process is noteworthy. Considerable time and human resources are required, particularly in regions with constrained resources, making it difficult to ensure that every individual receives prompt and accurate diagnostic attention.

3. Overview of BCI technology

BCI technology represents a pioneering advancement that enables direct communication between the human brain and external devices, achieved through the decipherment of neural activities [4]. Primarily utilizing techniques such as EEG and Functional Magnetic Resonance Imaging (fMRI), it captures and interprets brainwave signals, translating intricate cerebral impulses into actionable commands or quantifiable data.

3.1. Definition and basic principles

The architecture of a BCI comprises three principal components: signal acquisition, signal processing, and feedback mechanism. The signal acquisition phase involves the collection of raw brain activity data via EEG, fMRI, or other noninvasive or minimally invasive neural imaging modalities [5]. The feedback part transmits the decoded information to the external device to realize the information exchange between the human brain and the device (Figure 1). EEG is a noninvasive technique, which records brain waves by placing electrodes on the scalp. FMRI indirectly measures brain activity by detecting changes in brain blood flow. These technologies have been widely used in BCI.

Figure 1. The architecture of BCI comprises three principal components (Photo/Picture credit : Original)

3.2. Classification of BCI

BCI can be divided into noninvasive and invasive [6]. Noninvasive BCI records brain activity by placing electrodes on the scalp or using external imaging equipment, which has the advantages of safety and non-invasion, but the signal quality may be disturbed. Invasive BCI needs to implant electrodes into the cerebral cortex, which can obtain higher quality signals, but there are surgical risks and ethical problems. Different types of BCI have their own advantages and limitations in different application scenarios (Table 1) [7].

Table 1. Comparison between non-invasive BCI and invasive BCI.

3.3. The application of BCI in the diagnosis of ASD

• Neural activity pattern recognition

BCI can help doctors diagnose ASD by identifying the neural activity patterns of individual characteristics [8]. Studies have shown that there are significant differences in brain activity patterns between ASD patients and normal people. For example, in the process of social interaction and communication of ASD patients, EEG signals show abnormal synchronization and connectivity. By analyzing these neural activity data, BCI can identify the characteristic EEG patterns of ASD patients, thus improving the accuracy and efficiency of diagnosis [9].

• Empirical Case Analysis and Application Prospects

Recent studies indicate that EEG demonstrates significant success in distinguishing between ASD patients and typically developing individuals. For instance, research monitoring EEG signals during the viewing of social interaction videos has revealed distinct differences in brain activity patterns between ASD patients and normative individuals. These unique neural characteristics provide a reliable basis for the diagnosis of ASD, markedly enhancing the objectivity and accuracy of these assessments.

BCI technology also presents opportunities for enhancing social communication among individuals with ASD. Studies have shown that BCI systems can support children with ASD in improving their social skills. By leveraging specific brain waves—such as the P300 wave—patients can utilize BCI devices to select images or symbols to express themselves, significantly facilitating their social interaction and communication abilities. Moreover, research has explored BCI technology in the context of emotion recognition and regulation. These systems analyze EEG signals to identify patients' emotional states and provide tailored emotional management strategies, assisting them in better coping with emotional fluctuations and anxiety.

• Integration with Traditional Diagnostic Methods

The integration of BCI technology with conventional assessment tools, including questionnaires and clinical evaluations, can substantially enhance diagnostic accuracy. Traditional methods often focus on observable behaviors and symptoms, whereas BCI provides in-depth neural activity data. The synergistic combination of these approaches enables a comprehensive and multidimensional assessment of a patient's condition. For example, while conducting clinical observations, clinicians can leverage BCI systems to monitor patients' brain activity in real time, leading to more precise diagnostic conclusions.

• Potential for Early Intervention

The potential of BCI technology in early intervention for ASD is noteworthy. Through early screening, BCI can swiftly identify potential ASD risk factors, allowing clinicians to implement interventions before symptoms become apparent. For example, BCI could assess the brain responses of infants during social interactions to detect abnormal EEG patterns, enabling earlier intervention that improves future treatment outcomes.

4. Challenges and future outlook for BCI technology

4.1. Challenges

4.1.1. Technical challenges. The practical application of BCI faces several technical challenges. First, EEG signals are susceptible to external interference, which can compromise data quality. Second, the complexity of brain activity data necessitates advanced algorithms for accurate interpretation. Additionally, considerations related to the accessibility and affordability of BCI devices are critical; the high cost of quality devices hinders their adoption in resource-limited settings.

4.1.2. Ethical and privacy considerations. The application of BCI technology also raises important issues regarding data security and user privacy. Brain activity data is highly sensitive personal information, and it is essential to ensure that such data is not misused. Furthermore, ethical guidelines must be followed in clinical applications to ensure that patients are informed of their rights and have transparency about data usage.

4.2. Future directions and clinical application prospects

The future development of BCI technology requires continuous innovation. For instance, there is a need for the development of more advanced anti-interference technologies to enhance data quality. The application of multimodal data fusion techniques could help provide a more comprehensive understanding of brain activity by integrating EEG with other data sources, such as fMRI. Additionally, BCI has broad applications in personalized medicine, allowing for the customization of treatment plans based on individual patient profiles. BCI technology showcases extensive application prospects in the diagnosis and intervention of ASD, significantly improving clinical diagnostic efficiency and revealing substantial potential in personalized treatment and rehabilitation.

4.2.1. Tailored diagnostics in ASD. Conventional methods for diagnosing ASD often utilize a generalized approach that may not account for individual differences. In contrast, BCI technology can offer customized diagnostic insights based on distinct neural activity patterns. For instance, by examining the EEG signals of patients in various social contexts, healthcare professionals can more precisely identify specific challenges related to social interaction and communication. This enables the formulation of targeted therapeutic strategies.

4.2.2. Rehabilitation training and real-time feedback. BCI technology transcends mere diagnostic applications; it is instrumental in rehabilitation training. By continuously monitoring EEG activity, BCI can deliver real-time feedback, assisting individuals in modifying their behavioral and cognitive approaches. For example, during social skills training, BCI can analyze a participant’s EEG data during interactions and provide prompts to enhance social engagement, thereby boosting the effectiveness of the training [10].

4.2.3. Tele-health integration. The rise of tele-medicine expands the potential for BCI applications in diagnosing and treating ASD. Through remote connectivity, clinicians can observe EEG patterns in real time, even when physically distant from patients, allowing for precise diagnoses and tailored treatment recommendations. This capability is particularly beneficial for individuals in under-resourced regions, significantly improving access to healthcare services.

5. The role of interdisciplinary collaboration in BCI for ASD diagnosis

For BCI technology to be widely adopted in diagnosing ASD, collaboration across multiple disciplines is essential. Below are key fields and their contributions to BCI implementation:

• Neuroscience

Research in neuroscience underpins the development of BCI technologies. A deeper understanding of brain functions and neural activity provides insights essential for creating effective signal acquisition and analytical methods. For example, through studies utilizing animal models, researchers can investigate the involvement of specific neural pathways in ASD, informing how BCI technology can be applied.

• Engineering and Computer Science

Engineering and computer science are crucial for advancing BCI hardware and data analysis techniques. Engineers are responsible for creating high-performance EEG devices that ensure accurate and stable data collection. Concurrently, computer scientists design sophisticated algorithms to decode intricate EEG signals and extract relevant information.

• Clinical Medicine

Clinical practitioners play a vital role in the deployment and validation of BCI technologies. Through clinical trials, medical professionals can assess the efficacy of BCI in real-world diagnostic and therapeutic contexts, identifying and addressing any potential challenges. For instance, they can evaluate the technology among diverse age groups and symptom presentations in ASD patients to gauge its reliability and applicability.

• Psychology and Education

Insights from psychology and education provide a solid foundation for developing behavioral and cognitive training programs for individuals with ASD. By integrating BCI technology, psychologists and educators can create more impactful intervention and training modules. For instance, they may design interactive games and systems leveraging BCI to support children with ASD in enhancing their social and language skills.

6. Conclusion

This paper discusses the potential and application of BCI technology in the ASD. It is a complex neurodevelopmental disorder. Traditional diagnostic methods have some problems such as subjectivity and difficulty in early identification, so more objective and accurate diagnostic tools are urgently needed. BCI technology can provide real-time and objective neural activity data by directly reading and interpreting brain activity, thus providing support for early detection and personalized intervention of ASD.

This paper first introduces the clinical characteristics of ASD and the limitations of existing diagnostic methods, and then expounds the basic principle and classification of BCI technology in detail. By analyzing the differences between ASD patients and normal individuals in brain activity patterns, such as abnormal synchronization and connectivity of EEG signals, BCI can effectively identify the characteristic EEG patterns of ASD and improve the accuracy and efficiency of diagnosis. At the same time, BCI also demonstrated the application potential in social communication assistance and emotional management, such as helping ASD children improve their social skills and emotional adjustment ability through P300 brain waves. Further explore the complementary role of BCI and traditional diagnostic methods, and the application prospect in early intervention and personalized treatment. The paper also mentions the technical challenges, ethical issues and future development direction of BCI technology, such as improving the accuracy of signal acquisition and developing multimodal data fusion technology.

To sum up, BCI technology shows broad application prospects and importance in ASD diagnosis. Through interdisciplinary cooperation, including joint efforts in the fields of neuroscience, engineering and clinical medicine, it is expected to further promote the development of BCI technology and provide more accurate and personalized diagnosis and treatment programs for ASD patients.