Neurological Disorders Detection Based on Computer Brain Interface Using Centralized Blockchain with Intrusion System
| Autor: | Imam Yuwono, Eviani Damastuti, Utomo |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | International Journal of Communication Networks and Information Security (IJCNIS). 14:168-178 |
| ISSN: | 2073-607X 2076-0930 |
| DOI: | 10.17762/ijcnis.v14i2.5498 |
| Popis: | A brain-computer interface (BCI) would afford real-time communication, pointedly refining the standard of lifespan, brain-to-internet (B2I) connection, and interaction between the external digital devices and the brain. This assistive technology invents information and transmission advancement patterns, like directly linking the brain and multimedia gadgets to the cyber world. This system will convert brain data to signals which is understandable by multimedia gadgets without physical intervention and exchanges human-related languages with external atmosphere control protocols. These progressive difficulties would limit security severely. Hence, the rate of ransomware, attacks, malware, and other types of vulnerabilities will be rising radically. On the other hand, the necessity to enhance conventional processes for investigating cyberenvironment security facets. This article presents a Neurological Disorders Detection based on Computer Brain Interface Using Centralized Blockchain with Intrusion System (NDDCBI-CBIS). The projected NDDCBI-CBIS technique focuses on the identification of neurological disorders and epileptic seizure detection. To attain this, the presented NDDCBI-CBIS technique pre-processes the biomedical signals. Next, to detect epileptic seizures, long short-term memory (LSTM) model is applied. The experimental evaluation of the NDDCBI-CBIS approach can be tested by making use of the medical dataset and the outcomes inferred from the enhanced outcomes of the NDDCBI-CBIS technique. |
| Databáze: | OpenAIRE |
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