Electroencephalogram　(EEG)　is　widely　used　to　diagnose　many　neurological　and　psychiatric　brain　disorders.　The　correct　interpretation　of　EEG　data　is　critical　to　avoid　misdiagnosis.　However,　the　analysis　of　EEG　data　requires　trained　specialists　and　may　vary　from　expert　to　expert.　Meanwhile,　it　can　be　challenging　and　time-consuming　to　assess　the　EEG　data　since　these　signals　may　last　several　hours　or　days.　Therefore,　rapid　and　accurate　classification　of　EEG　data　may　be　a　key　step　towards　interpreting　EEG　records.　In　this　study,　a　novel　deep　learning　model　with　an　end-to-end　structure　is　proposed　to　distinguish　normal　and　abnormal　EEG　signals　automatically.　For　this　purpose,　we　investigate　the　possibility　of　combining　the　core　ideas　of　inception　and　residual　architectures　into　a　hybrid　model　to　improve　classification　performance.　We　evaluated　the　proposed　method　through　extensive　experiments　on　a　real-world　dataset,　and　it　shows　feasibility　and　effectiveness.　Compared　to　previous　studies　on　the　same　data,　our　method　outperforms　other　existing　EEG　signal　methods.　Thus,　the　proposed　method　can　aid　clinicians　to　automatically　detect　brain　activity.　©　2021　IEEE.