KR-20260064005-A - Device and method for diagnosing depression using P300 signal analysis based on xDAWN

Abstract

The present invention presents a method for measuring P300 responses from EEG signals, improving the signal-to-noise ratio by applying an xDAWN spatial filter, and analyzing the magnitude and latency of the P300. The device includes a mechanism for evaluating the potential for depression by collecting EEG signals through Cz and Pz electrodes and analyzing the characteristics of the P300 after xDAWN filtering. The present invention is implemented in a form that is easy for the user to operate and provides a device capable of objectively and rapidly evaluating a subject's mental health status. Furthermore, the present invention provides the analysis results of the P300 signal as visual or auditory feedback, enabling the user to clearly understand their mental health status and take appropriate measures if necessary. A key feature of the present invention is that it optimizes the signal-to-noise ratio by applying high-performance amplifiers and noise filtering techniques to maximize the quality of EEG signals, thereby enhancing the reliability of depression diagnosis. Furthermore, the present invention is designed to be suitable for both personal and clinical use, allowing it to be utilized in various environments and significantly improving the efficiency of mental health management. This enables the early detection of depression and facilitates therapeutic intervention at an early stage.

Inventors

• 이창동

Assignees

• 이창동

Dates

Publication Date

20260507

Application Date

20241031

Claims (4)

1. In a device for collecting EEG signals of a subject using Cz and Pz electrodes and recording P300 event-related potentials from a response to an auditory stimulus, A signal processing device characterized by including a high-performance amplifier and a noise filter to improve signal quality.
2. In Article 1, The above recording device is, A signal processing device characterized by improving the signal-to-noise ratio (SNR) by applying an xDAWN spatial filter to enhance the P300 response from the collected EEG signal.
3. In Paragraph 2, A signal analysis method for evaluating a subject's cognitive state and potential for depression by analyzing the magnitude and latency of P300 in the above xDAWN-filtered EEG data A signal processing device characterized by including a statistical processing technique for obtaining reliable results through repeated measurements.
4. In Paragraph 3, The P300 signal analyzed above is, A signal processing device characterized by determining whether a subject has depression based on size and latency values, evaluating the user's mental health status by synthesizing analysis results, and collecting additional evaluation data to increase the reliability of the diagnosis.

Description

Device and method for diagnosing depression using P300 signal analysis based on xDAWN The present invention relates to a technology for diagnosing depression through the analysis of electroencephalogram (EEG) signals, and specifically belongs to the field of technology concerning methods and devices for diagnosing depression by combining P300 event-related potentials with the xDAWN algorithm. The present invention is also a technology focused on optimizing the quality of EEG signals through the improvement of the signal-to-noise ratio (SNR), providing an important diagnostic tool that can be utilized in various biosignal processing and mental health management. Through this, more effective and reliable diagnosis of depression is possible in the field of mental health, and a practical diagnostic solution applicable in both individual and clinical settings is presented. Depression is one of the major mental illnesses that severely affects cognitive function and emotions, making early diagnosis and treatment crucial. Currently, the diagnosis of depression relies primarily on psychological testing and subjective evaluations by clinicians; however, these methods have limitations, such as being time-consuming and costly, and making it difficult to base diagnoses on objective data. To overcome these limitations, there is a growing need for technologies that diagnose depression by analyzing biological signals. Diagnostic technology utilizing EEG signals is considered a highly promising approach for addressing these constraints. The P300 signal is an event-related potential reflecting a cognitive response to a stimulus, and it is widely used to assess cognitive ability and attention span. The P300 response is a potential generated during the cognitive processing of a specific stimulus, and analyzing it allows for the quantitative evaluation of a subject's cognitive function. Patients with depression generally exhibit changes in the amplitude and latency of their P300 responses compared to healthy individuals, and these changes reflect a decline in the speed and efficiency of cognitive processing. Therefore, the P300 signal can serve as an important indicator for the diagnosis of depression. In particular, a decrease in the magnitude of the P300 response and a delay in the latency period indicate a decline in brain function in patients with depression, which is associated with a reduction in cognitive resources. By analyzing these signal characteristics, the cognitive processing ability and responsiveness to stimuli of patients with depression can be evaluated, and quantifying these factors can enhance the objectivity of diagnosis. This can be utilized as a reliable indicator, particularly in evaluating diverse patients in clinical settings. xDAWN spatial filtering technology improves the analysis accuracy of P300 signals by enhancing the signal-to-noise ratio (SNR) across multiple trials of EEG signals. This effectively removes noise elements to increase signal validity and enables clearer detection of P300 signals. Since EEG data is sensitive to various external environmental noises, removing such noise is essential for accurate signal analysis. The xDAWN algorithm helps to detect and analyze P300 responses more clearly by enhancing signal characteristics. By combining these P300 signals with the xDAWN algorithm, this invention provides a method and device for more efficiently diagnosing depression. As a diagnostic system that can be easily used at home, it is useful for early diagnosis and self-management. Furthermore, this invention has the advantage of being cost-effective and designed for user-friendly utilization. FIG. 1 is a conceptual diagram schematically showing the configuration of an EEG signal acquisition device according to an embodiment of the present invention. Figure 2 is a diagram showing the signal enhancement process through xDAWN filtering. The advantages and/or features of the present invention and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components. Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. FIG. 1 is a conceptual diagram schematically showing the configuration of an EEG signal acquisition device according to an embodiment of the present invention, and FIG. 2 is a diagram showing the signal improvement process through xDAWN filtering. First, referring to Fig. 1, E