Brain-computer　interfaces　(BCIs)　establish　a　connection　between　the　human　brain　and　external　devices,　facilitating　novel　forms　of　communication　and　control.　Although　BCIs　possess　significant　potential,　there　is　a　need　for　performance　improvements.　Integrating　neural　networks　into　BCIs　is　crucial　for　enhancing　functionality　and　promoting　broader　adoption.　This　study　examines　1,867　articles　from　the　Web　of　Science　core　database,　covering　the　period　from　1996　to　2024,　to　identify　contemporary　hotspots　and　trends　in　the　application　of　neural　networks　within　BCIs.　This　study　employs　bibliometric　methods　and　Python　for　analysis,　examining　collaborative　relationships,　citation　networks,　keyword　bursts,　and　clustering　with　visual　representations　of　the　findings.　The　results　indicated　that　current　study　hotspots　predominantly　center　on　＂P300,＂　＂Long　Short-Term　Memory,＂　＂Motor　Imagery,＂　＂Epilepsy,＂　＂Emotion　Recognition,＂　＂Feature　Extraction,＂　and　＂Transfer　Learning.＂　Future　development　directions　encompass:　(1)　the　establishment　of　public　BCI　datasets;　(2)　the　exploration　of　diverse　feature　extraction　and　fusion　methods;　(3)　the　enhancement　of　machine　learning　and　deep　learning　integration　for　improved　performance　and　real-time　processing;　(4)　the　expansion　of　application　scenarios　and　the　development　of　portable　devices;　(5)　the　optimization　of　transfer　learning　algorithms　to　mitigate　performance　challenges　arising　from　individual　differences.　This　study　provides　an　overview　of　the　current　research　landscape　and　identifies　potential　future　research　directions.　Furthermore,　it　assists　practitioners　in　recognizing　additional　business　opportunities　and　acts　as　a　resource　for　the　formulation　of　government　policy.