Mạng nơ-ron đỉnh không gian cho phân loại tín hiệu EEG trong bài kiểm tra thông tin ẩn giấu

Damoder Reddy Edla1, Annushree Bablani2, Saugat Bhattacharyya3, Ramesh Dharavath4, Ramalingaswamy Cheruku5, Vijayasree Boddu6
1Department of CSE, National Institute of Technology Goa, Veling, India
2Department of CSE, Indian Institute of Information Technology Sricity, Sathyavedu, India
3Computer Science, SCEIS, Ulster University, Magee Campus, Londonderry, UK
4Department of CSE, Indian Institute of Technology (ISM), Dhanbad, Jharkand, India
5Department of CSE, National Institute of Technology Warangal, Warangal, India
6Department of ECE, National Institute of Technology Warangal, Warangal, India

Tóm tắt

Trong lĩnh vực thần kinh học, một thách thức đáng kể là trích xuất các đặc điểm thiết yếu từ các tín hiệu sinh học như Điện não đồ (EEG). Được sử dụng như một phương pháp không xâm lấn, EEG ghi lại hoạt động của não thông qua các điện cực kim loại trên da đầu. Phân tích dữ liệu EEG tìm thấy ứng dụng trong nhiều lĩnh vực khác nhau, bao gồm các bài kiểm tra thông tin ẩn giấu, nhằm phát hiện sự dối trá. Bài báo này giới thiệu Mạng nơ-ron đỉnh không gian, một phương pháp có giám sát để phân loại dữ liệu EEG thu thập được trong các bài kiểm tra thông tin ẩn giấu. Dữ liệu EEG tạm thời được lọc bằng bộ lọc phản hồi xung hữu hạn (FIR), trong khi Mô hình không gian chung (CSP) được sử dụng để trích xuất các thành phần không gian. Phân loại nhị phân được thực hiện thông qua một mô hình nơ-ron tích hợp và bóp cò, nơi tần suất tạo ra xung xác định phân loại. Mạng nơ-ron đỉnh (SNNs) cung cấp những lợi thế về độ chính xác tạm thời, xử lý dựa trên sự kiện và tiêu thụ năng lượng thấp. Giao tiếp dựa trên xung của chúng cho phép xử lý hiệu quả dữ liệu thưa thớt và nhận diện các mẫu tạm thời, góp phần vào độ bền và hiệu quả năng lượng. Mô hình đề xuất được áp dụng riêng biệt cho dữ liệu EEG của mỗi đối tượng, và kết quả được so sánh với các thuật toán phân loại truyền thống. Phương pháp đề xuất đạt được độ chính xác cao nhất là 90.15%, cho thấy hiệu suất vượt trội so với các phương pháp thay thế.

Từ khóa

#thần kinh học #Điện não đồ #kiểm tra thông tin ẩn giấu #phân loại nhị phân #mạng nơ-ron đỉnh #mô hình không gian chung

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