Large-area　tactile　sensors　based　on　the　technique　of　electrical　impedance　tomography　(EIT)　has　drawn　considerable　interest　in　human-robot　interactions.　However,　due　to　the　ill-posed　condition,　it　is　challenging　to　differentiate　between　the　real　contacts　and　the　artifacts　from　the　reconstructed　image.　To　address　this　issue,　a　new　method　to　select　an　optimal　hyperparameter　that　tunes　the　amount　of　regularization　is　developed　in　the　context　of　tactile　sensing.　The　optimal　hyperparameter　is　determined　to　be　the　minimum　value　to　obtain　a　stabilized　number　of　sub-regions　in　the　reconstructed　image.　The　proposed　method　not　only　guarantees　a　correct　detection　on　the　number　of　multiple　contacts　at　the　minimum　amount　of　regularization,　but　also　provides　a　proper　range　of　hyperparameters.　The　optimal　hyperparameter　is　found　in　a　chair-shape　relation　with　the　boundary　signal-to-noise　ratio　(SNR),　by　varying　the　noise　level　of　the　hardware　in　simulation.　The　optimal　hyperparameter　decreases　significantly　when　the　boundary　SNR　increases　between　5　∼　10　dB　and　25　∼　35　dB,　and　keeps　almost　unchanged　when　SNR　is　between　10　∼　25　dB.　The　chair-shape　relation　also　holds　for　contact　conditions　with　varied　intensities　and　sizes.　Experimental　validations　on　the　proposed　method　are　conducted　on　a　compliant　piezoresistive　tactile　sensor　made　of　exfoliated　graphite　polymer　composites.　By　varying　the　number　of　contacts　in　experiments,　the　relation　between　the　optimal　hyperparameter　and　the　boundary　SNR　is　consistent　with　the　chair-shape　curve.　The　investigation　made　in　this　work　helps　improve　the　performance　of　identifying　multiple　contacts　from　tactile　sensors　based　on　electrical　impedance　tomography.　©　2023　IOP　Publishing　Ltd.