{"author_name":"chaos_kiyono","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/22/012359","description":"In signal processing, it is very common to encounter situations where signals observed by multiple sensors are, in fact, mixtures of several independent \u201csources.\u201d Problems of this type are known as Blind Source Separation (BSS). Among BSS methods, Independent Component Analysis (ICA) is widely know\u2026","provider_url":"https://hatena.blog","blog_url":"https://chaos-r.hatenadiary.jp/","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F22%2F012359\" title=\"Blind Source Separation in R: An Introduction to Second-Order Blind Identification (SOBI) - Ken-Chaos\u2019s Random Notes on R\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","blog_title":"Ken-Chaos\u2019s Random Notes on R","categories":["R","time series analysis","stochastic process"],"image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20260121/20260121234440.png","height":"190","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","version":"1.0","width":"100%","title":"Blind Source Separation in R: An Introduction to Second-Order Blind Identification (SOBI)","provider_name":"Hatena Blog","published":"2026-01-22 01:23:59","type":"rich"}